From 46321f137ea6e91d4c0c780213c33b56abffa522 Mon Sep 17 00:00:00 2001 From: Mark Valdez <1289198+valdezm@users.noreply.github.com> Date: Wed, 14 May 2025 11:44:25 -0700 Subject: [PATCH 01/18] Update README for WSL UVX usage --- README.md | 269 +++++++++++++++++++++++++++--------------------------- 1 file changed, 137 insertions(+), 132 deletions(-) diff --git a/README.md b/README.md index 9cd0a523..1cbe0b40 100644 --- a/README.md +++ b/README.md @@ -42,11 +42,13 @@ For additional background on why we built Postgres MCP Pro, see [our launch blog ## Demo -*From Unusable to Lightning Fast* +_From Unusable to Lightning Fast_ + - **Challenge:** We generated a movie app using an AI assistant, but the SQLAlchemy ORM code ran painfully slow. - **Solution:** Using Postgres MCP Pro with Cursor, we fixed the performance issues in minutes. What we did: + - πŸš€ Fixed performance - including ORM queries, indexing, and caching - πŸ› οΈ Fixed a broken page - by prompting the agent to explore the data, fix queries, and add related content. - 🧠 Improved the top movies - by exploring the data and fixing the ORM query to surface more relevant results. @@ -55,20 +57,18 @@ See the video below or read the [play-by-play](examples/movie-app.md). https://github.com/user-attachments/assets/24e05745-65e9-4998-b877-a368f1eadc13 - - - ## Quick Start ### Prerequisites Before getting started, ensure you have: + 1. Access credentials for your database. -2. Docker *or* Python 3.12 or higher. +2. Docker _or_ Python 3.12 or higher. #### Access Credentials - You can confirm your access credentials are valid by using `psql` or a GUI tool such as [pgAdmin](https://www.pgadmin.org/). +You can confirm your access credentials are valid by using `psql` or a GUI tool such as [pgAdmin](https://www.pgadmin.org/). #### Docker or Python @@ -76,7 +76,6 @@ The choice to use Docker or Python is yours. We generally recommend Docker because Python users can encounter more environment-specific issues. However, it often makes sense to use whichever method you are most familiar with. - ### Installation Choose one of the following methods to install Postgres MCP Pro: @@ -90,7 +89,6 @@ This image contains all necessary dependencies, providing a reliable way to run docker pull crystaldba/postgres-mcp ``` - #### Option 2: Using Python If you have `pipx` installed you can install Postgres MCP Pro with: @@ -107,7 +105,6 @@ uv pip install postgres-mcp If you need to install `uv`, see the [uv installation instructions](https://docs.astral.sh/uv/getting-started/installation/). - ### Configure Your AI Assistant We provide full instructions for configuring Postgres MCP Pro with Claude Desktop. @@ -117,6 +114,7 @@ Many MCP clients have similar configuration files, you can adapt these steps to You will need to edit the Claude Desktop configuration file to add Postgres MCP Pro. The location of this file depends on your operating system: + - MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json` - Windows: `%APPDATA%/Claude/claude_desktop_config.json` @@ -124,6 +122,23 @@ You can also use `Settings` menu item in Claude Desktop to locate the configurat You will now edit the `mcpServers` section of the configuration file. +#### Windows WSL(using UVX, no need to install anything) + +```json +{ + "mcpServers": { + "postgres": { + "command": "wsl.exe", + "args": [ + "bash", + "-c", + "/home/mark/.local/bin/uvx --from git+https://github.com/crystaldba/postgres-mcp@main postgres-mcp postgresql://username:password@localhost:5432/dbname --access-mode=unrestricted" + ] + } + } +} +``` + ##### If you are using Docker ```json @@ -153,7 +168,6 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho - MacOS/Windows: Uses `host.docker.internal` automatically - Linux: Uses `172.17.0.1` or the appropriate host address automatically - ##### If you are using `pipx` ```json @@ -161,9 +175,7 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho "mcpServers": { "postgres": { "command": "postgres-mcp", - "args": [ - "--access-mode=unrestricted" - ], + "args": ["--access-mode=unrestricted"], "env": { "DATABASE_URI": "postgresql://username:password@localhost:5432/dbname" } @@ -172,7 +184,6 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho } ``` - ##### If you are using `uv` ```json @@ -180,11 +191,7 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho "mcpServers": { "postgres": { "command": "uv", - "args": [ - "run", - "postgres-mcp", - "--access-mode=unrestricted" - ], + "args": ["run", "postgres-mcp", "--access-mode=unrestricted"], "env": { "DATABASE_URI": "postgresql://username:password@localhost:5432/dbname" } @@ -193,21 +200,19 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho } ``` - ##### Connection URI Replace `postgresql://...` with your [Postgres database connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS). - ##### Access Mode -Postgres MCP Pro supports multiple *access modes* to give you control over the operations that the AI agent can perform on the database: +Postgres MCP Pro supports multiple _access modes_ to give you control over the operations that the AI agent can perform on the database: + - **Unrestricted Mode**: Allows full read/write access to modify data and schema. It is suitable for development environments. - **Restricted Mode**: Limits operations to read-only transactions and imposes constraints on resource utilization (presently only execution time). It is suitable for production environments. To use restricted mode, replace `--access-mode=unrestricted` with `--access-mode=restricted` in the configuration examples above. - #### Other MCP Clients Many MCP clients have similar configuration files to Claude Desktop, and you can adapt the examples above to work with the client of your choice. @@ -234,12 +239,12 @@ For example, in Cursor's `mcp.json` or Cline's `cline_mcp_settings.json` you can ```json { - "mcpServers": { - "postgres": { - "type": "sse", - "url": "http://localhost:8000/sse" - } + "mcpServers": { + "postgres": { + "type": "sse", + "url": "http://localhost:8000/sse" } + } } ``` @@ -247,12 +252,12 @@ For Windsurf, the format in `mcp_config.json` is slightly different: ```json { - "mcpServers": { - "postgres": { - "type": "sse", - "serverUrl": "http://localhost:8000/sse" - } + "mcpServers": { + "postgres": { + "type": "sse", + "serverUrl": "http://localhost:8000/sse" } + } } ``` @@ -261,7 +266,7 @@ For Windsurf, the format in `mcp_config.json` is slightly different: To enable index tuning and comprehensive performance analysis you need to load the `pg_statements` and `hypopg` extensions on your database. - The `pg_statements` extension allows Postgres MCP Pro to analyze query execution statistics. -For example, this allows it to understand which queries are running slow or consuming significant resources. + For example, this allows it to understand which queries are running slow or consuming significant resources. - The `hypopg` extension allows Postgres MCP Pro to simulate the behavior of the Postgres query planner after adding indexes. ### Installing extensions on AWS RDS, Azure SQL, or Google Cloud SQL @@ -285,26 +290,31 @@ The `hypopg` extension may also require additional system-level installation (e. ### Get Database Health Overview Ask: + > Check the health of my database and identify any issues. ### Analyze Slow Queries Ask: + > What are the slowest queries in my database? And how can I speed them up? ### Get Recommendations On How To Speed Things Up Ask: + > My app is slow. How can I make it faster? ### Generate Index Recommendations Ask: + > Analyze my database workload and suggest indexes to improve performance. ### Optimize a Specific Query Ask: + > Help me optimize this query: SELECT \* FROM orders JOIN customers ON orders.customer_id = customers.id WHERE orders.created_at > '2023-01-01'; ## MCP Server API @@ -315,25 +325,24 @@ Postgres MCP Pro provides functionality via [MCP tools](https://modelcontextprot We chose this approach because the [MCP client ecosystem](https://modelcontextprotocol.io/clients) has widespread support for MCP tools. This contrasts with the approach of other Postgres MCP servers, including the [Reference Postgres MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres), which use [MCP resources](https://modelcontextprotocol.io/docs/concepts/resources) to expose schema information. - Postgres MCP Pro Tools: -| Tool Name | Description | -|-----------|-------------| -| `list_schemas` | Lists all database schemas available in the PostgreSQL instance. | -| `list_objects` | Lists database objects (tables, views, sequences, extensions) within a specified schema. | -| `get_object_details` | Provides information about a specific database object, for example, a table's columns, constraints, and indexes. | -| `execute_sql` | Executes SQL statements on the database, with read-only limitations when connected in restricted mode. | -| `explain_query` | Gets the execution plan for a SQL query describing how PostgreSQL will process it and exposing the query planner's cost model. Can be invoked with hypothetical indexes to simulate the behavior after adding indexes. | -| `get_top_queries` | Reports the slowest SQL queries based on total execution time using `pg_stat_statements` data. | -| `analyze_workload_indexes` | Analyzes the database workload to identify resource-intensive queries, then recommends optimal indexes for them. | -| `analyze_query_indexes` | Analyzes a list of specific SQL queries (up to 10) and recommends optimal indexes for them. | -| `analyze_db_health` | Performs comprehensive health checks including: buffer cache hit rates, connection health, constraint validation, index health (duplicate/unused/invalid), sequence limits, and vacuum health. | - +| Tool Name | Description | +| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `list_schemas` | Lists all database schemas available in the PostgreSQL instance. | +| `list_objects` | Lists database objects (tables, views, sequences, extensions) within a specified schema. | +| `get_object_details` | Provides information about a specific database object, for example, a table's columns, constraints, and indexes. | +| `execute_sql` | Executes SQL statements on the database, with read-only limitations when connected in restricted mode. | +| `explain_query` | Gets the execution plan for a SQL query describing how PostgreSQL will process it and exposing the query planner's cost model. Can be invoked with hypothetical indexes to simulate the behavior after adding indexes. | +| `get_top_queries` | Reports the slowest SQL queries based on total execution time using `pg_stat_statements` data. | +| `analyze_workload_indexes` | Analyzes the database workload to identify resource-intensive queries, then recommends optimal indexes for them. | +| `analyze_query_indexes` | Analyzes a list of specific SQL queries (up to 10) and recommends optimal indexes for them. | +| `analyze_db_health` | Performs comprehensive health checks including: buffer cache hit rates, connection health, constraint validation, index health (duplicate/unused/invalid), sequence limits, and vacuum health. | ## Related Projects **Postgres MCP Servers** + - [Query MCP](https://github.com/alexander-zuev/supabase-mcp-server). An MCP server for Supabase Postgres with a three-tier safety architecture and Supabase management API support. - [PG-MCP](https://github.com/stuzero/pg-mcp-server). An MCP server for PostgreSQL with flexible connection options, explain plans, extension context, and more. - [Reference PostgreSQL MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres). A simple MCP Server implementation exposing schema information as MCP resources and executing read-only queries. @@ -343,6 +352,7 @@ Postgres MCP Pro Tools: - [Wren MCP Server](https://github.com/Canner/wren-engine). Provides a semantic engine powering business intelligence for Postgres and other databases. **DBA Tools (including commercial offerings)** + - [Aiven Database Optimizer](https://aiven.io/solutions/aiven-ai-database-optimizer). A tool that provides holistic database workload analysis, query optimizations, and other performance improvements. - [dba.ai](https://www.dba.ai/). An AI-powered database administration assistant that integrates with GitHub to resolve code issues. - [pgAnalyze](https://pganalyze.com/). A comprehensive monitoring and analytics platform for identifying performance bottlenecks, optimizing queries, and real-time alerting. @@ -350,49 +360,49 @@ Postgres MCP Pro Tools: - [Xata Agent](https://github.com/xataio/agent). An open-source AI agent that automatically monitors database health, diagnoses issues, and provides recommendations using LLM-powered reasoning and playbooks. **Postgres Utilities** + - [Dexter](https://github.com/DexterDB/dexter). A tool for generating and testing hypothetical indexes on PostgreSQL. - [PgHero](https://github.com/ankane/pghero). A performance dashboard for Postgres, with recommendations. -Postgres MCP Pro incorporates health checks from PgHero. + Postgres MCP Pro incorporates health checks from PgHero. - [PgTune](https://github.com/le0pard/pgtune?tab=readme-ov-file). Heuristics for tuning Postgres configuration. ## Frequently Asked Questions -*How is Postgres MCP Pro different from other Postgres MCP servers?* +_How is Postgres MCP Pro different from other Postgres MCP servers?_ There are many MCP servers allow an AI agent to run queries against a Postgres database. Postgres MCP Pro does that too, but also adds tools for understanding and improving the performance of your Postgres database. For example, it implements a version of the [Anytime Algorithm of Database Tuning Advisor for Microsoft SQL Server](https://www.microsoft.com/en-us/research/wp-content/uploads/2020/06/Anytime-Algorithm-of-Database-Tuning-Advisor-for-Microsoft-SQL-Server.pdf), a modern industrial-strength algorithm for automatic index tuning. -| Postgres MCP Pro | Other Postgres MCP Servers | -|--------------|----------------------------| -| βœ… Deterministic database health checks | ❌ Unrepeatable LLM-generated health queries | -| βœ… Principled indexing search strategies | ❌ Gen-AI guesses at indexing improvements | -| βœ… Workload analysis to find top problems | ❌ Inconsistent problem analysis | -| βœ… Simulates performance improvements | ❌ Try it yourself and see if it works | +| Postgres MCP Pro | Other Postgres MCP Servers | +| ----------------------------------------- | -------------------------------------------- | +| βœ… Deterministic database health checks | ❌ Unrepeatable LLM-generated health queries | +| βœ… Principled indexing search strategies | ❌ Gen-AI guesses at indexing improvements | +| βœ… Workload analysis to find top problems | ❌ Inconsistent problem analysis | +| βœ… Simulates performance improvements | ❌ Try it yourself and see if it works | Postgres MCP Pro complements generative AI by adding deterministic tools and classical optimization algorithms The combination is both reliable and flexible. - -*Why are MCP tools needed when the LLM can reason, generate SQL, etc?* +_Why are MCP tools needed when the LLM can reason, generate SQL, etc?_ LLMs are invaluable for tasks that involve ambiguity, reasoning, or natural language. When compared to procedural code, however, they can be slow, expensive, non-deterministic, and sometimes produce unreliable results. In the case of database tuning, we have well established algorithms, developed over decades, that are proven to work. Postgres MCP Pro lets you combine the best of both worlds by pairing LLMs with classical optimization algorithms and other procedural tools. -*How do you test Postgres MCP Pro?* +_How do you test Postgres MCP Pro?_ Testing is critical to ensuring that Postgres MCP Pro is reliable and accurate. We are building out a suite of AI-generated adversarial workloads designed to challenge Postgres MCP Pro and ensure it performs under a broad variety of scenarios. -*What Postgres versions are supported?* +_What Postgres versions are supported?_ Our testing presently focuses on Postgres 15, 16, and 17. We plan to support Postgres versions 13 through 17. -*Who created this project?* +_Who created this project?_ This project is created and maintained by [Crystal DBA](https://www.crystaldba.ai). ## Roadmap -*TBD* +_TBD_ You and your needs are a critical driver for what we build. Tell us what you want to see by opening an [issue](https://github.com/crystaldba/postgres-mcp/issues) or a [pull request](https://github.com/crystaldba/postgres-mcp/pulls). @@ -406,62 +416,62 @@ This section includes a high-level overview technical considerations that influe Developers know that missing indexes are one of the most common causes of database performance issues. Indexes provide access methods that allow Postgres to quickly locate data that is required to execute a query. -When tables are small, indexes make little difference, but as the size of the data grows, the difference in algorithmic complexity between a table scan and an index lookup becomes significant (typically *O*(*n*) vs *O*(*log* *n*), potentially more if joins on multiple tables are involved). +When tables are small, indexes make little difference, but as the size of the data grows, the difference in algorithmic complexity between a table scan and an index lookup becomes significant (typically _O_(_n_) vs _O_(_log_ _n_), potentially more if joins on multiple tables are involved). Generating suggested indexes in Postgres MCP Pro proceeds in several stages: -1. *Identify SQL queries in need of tuning*. - If you know you are having a problem with a specific SQL query you can provide it. - Postgres MCP Pro can also analyze the workload to identify index tuning targets. - To do this, it relies on the `pg_stat_statements` extension, which records the runtime and resource consumption of each query. - - A query is a candidate for index tuning if it is a top resource consumer, either on a per-execution basis or in aggregate. - At present, we use execution time as a proxy for cumulative resource consumption, but it may also make sense to look at specifics resources, e.g., the number of blocks accessed or the number of blocks read from disk. - The `analyze_query_workload` tool focuses on slow queries, using the mean time per execution with thresholds for execution count and mean execution time. - Agents may also call `get_top_queries`, which accepts a parameter for mean vs. total execution time, then pass these queries `analyze_query_indexes` to get index recommendations. - - Sophisticated index tuning systems use "workload compression" to produce a representative subset of queries that reflects the characteristics of the workload as a whole, reducing the problem for downstream algorithms. - Postgres MCP Pro performs a limited form of workload compression by normalizing queries so that those generated from the same template appear as one. - It weights each query equally, a simplification that works when the benefits to indexing are large. - -2. *Generate candidate indexes* - Once we have a list of SQL queries that we want to improve through indexing, we generate a list of indexes that we might want to add. - To do this, we parse the SQL and identify any columns used in filters, joins, grouping, or sorting. - - To generate all possible indexes we need to consider combinations of these columns, because Postgres supports [multicolumn indexes](https://www.postgresql.org/docs/current/indexes-multicolumn.html). - In the present implementation, we include only one permutation of each possible multicolumn index, which is selected at random. - We make this simplification to reduce the search space because permutations often have equivalent performance. - However, we hope to improve in this area. - -3. *Search for the optimal index configuration*. - Our objective is to find the combination of indexes that optimally balances the performance benefits against the costs of storing and maintaining those indexes. - We estimate the performance improvement by using the "what if?" capabilities provided by the `hypopg` extension. - This simulates how the Postgres query optimizer will execute a query after the addition of indexes, and reports changes based on the actual Postgres cost model. - - One challenge is that generating query plans generally requires knowledge of the specific parameter values used in the query. - Query normalization, which is necessary to reduce the queries under consideration, removes parameter constants. - Parameter values provided via bind variables are similarly not available to us. - - To address this problem, we produce realistic constants that we can provide as parameters by sampling from the table statistics. - In version 16, Postgres added [generic explain plan functionality](https://www.postgresql.org/docs/current/sql-explain.html), but it has limitations, for example around `LIKE` clauses, which our implementation does not have. - - Search strategy is critical because evaluating all possible index combinations feasible only in simple situations. - This is what most sets apart various indexing approaches. - Adapting the approach of Microsoft's Anytime algorithm, we employ a greedy search strategy, i.e., find the best one-index solution, then find the best index to add to that to produce a two-index solution. - Our search terminates when the time budget is exhausted or when a round of exploration fails to produce any gains above the minimum improvement threshold of 10%. - -4. *Cost-benefit analysis*. - When posed with two indexing alternatives, one which produces better performance and one which requires more space, how do we decide which to choose? - Traditionally, index advisors ask for a storage budget and optimize performance with respect to that storage budget. - We also take a storage budget, but perform a cost-benefit analysis throughout the optimization. - - We frame this as the problem of selecting a point along the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front)β€”the set of choices for which improving one quality metric necessarily worsens another. - In an ideal world, we might want to assess the cost of the storage and the benefit of improved performance in monetary terms. - However, there is a simpler and more practical approach: to look at the changes in relative terms. - Most people would agree that a 100x performance improvement is worth it, even if the storage cost is 2x. - In our implementation, we use a configurable parameter to set this threshold. - By default, we require the change in the log (base 10) of the performance improvement to be 2x the difference in the log of the space cost. - This works out to allowing a maximum 10x increase in space for a 100x performance improvement. +1. _Identify SQL queries in need of tuning_. + If you know you are having a problem with a specific SQL query you can provide it. + Postgres MCP Pro can also analyze the workload to identify index tuning targets. + To do this, it relies on the `pg_stat_statements` extension, which records the runtime and resource consumption of each query. + + A query is a candidate for index tuning if it is a top resource consumer, either on a per-execution basis or in aggregate. + At present, we use execution time as a proxy for cumulative resource consumption, but it may also make sense to look at specifics resources, e.g., the number of blocks accessed or the number of blocks read from disk. + The `analyze_query_workload` tool focuses on slow queries, using the mean time per execution with thresholds for execution count and mean execution time. + Agents may also call `get_top_queries`, which accepts a parameter for mean vs. total execution time, then pass these queries `analyze_query_indexes` to get index recommendations. + + Sophisticated index tuning systems use "workload compression" to produce a representative subset of queries that reflects the characteristics of the workload as a whole, reducing the problem for downstream algorithms. + Postgres MCP Pro performs a limited form of workload compression by normalizing queries so that those generated from the same template appear as one. + It weights each query equally, a simplification that works when the benefits to indexing are large. + +2. _Generate candidate indexes_ + Once we have a list of SQL queries that we want to improve through indexing, we generate a list of indexes that we might want to add. + To do this, we parse the SQL and identify any columns used in filters, joins, grouping, or sorting. + + To generate all possible indexes we need to consider combinations of these columns, because Postgres supports [multicolumn indexes](https://www.postgresql.org/docs/current/indexes-multicolumn.html). + In the present implementation, we include only one permutation of each possible multicolumn index, which is selected at random. + We make this simplification to reduce the search space because permutations often have equivalent performance. + However, we hope to improve in this area. + +3. _Search for the optimal index configuration_. + Our objective is to find the combination of indexes that optimally balances the performance benefits against the costs of storing and maintaining those indexes. + We estimate the performance improvement by using the "what if?" capabilities provided by the `hypopg` extension. + This simulates how the Postgres query optimizer will execute a query after the addition of indexes, and reports changes based on the actual Postgres cost model. + + One challenge is that generating query plans generally requires knowledge of the specific parameter values used in the query. + Query normalization, which is necessary to reduce the queries under consideration, removes parameter constants. + Parameter values provided via bind variables are similarly not available to us. + + To address this problem, we produce realistic constants that we can provide as parameters by sampling from the table statistics. + In version 16, Postgres added [generic explain plan functionality](https://www.postgresql.org/docs/current/sql-explain.html), but it has limitations, for example around `LIKE` clauses, which our implementation does not have. + + Search strategy is critical because evaluating all possible index combinations feasible only in simple situations. + This is what most sets apart various indexing approaches. + Adapting the approach of Microsoft's Anytime algorithm, we employ a greedy search strategy, i.e., find the best one-index solution, then find the best index to add to that to produce a two-index solution. + Our search terminates when the time budget is exhausted or when a round of exploration fails to produce any gains above the minimum improvement threshold of 10%. + +4. _Cost-benefit analysis_. + When posed with two indexing alternatives, one which produces better performance and one which requires more space, how do we decide which to choose? + Traditionally, index advisors ask for a storage budget and optimize performance with respect to that storage budget. + We also take a storage budget, but perform a cost-benefit analysis throughout the optimization. + + We frame this as the problem of selecting a point along the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front)β€”the set of choices for which improving one quality metric necessarily worsens another. + In an ideal world, we might want to assess the cost of the storage and the benefit of improved performance in monetary terms. + However, there is a simpler and more practical approach: to look at the changes in relative terms. + Most people would agree that a 100x performance improvement is worth it, even if the storage cost is 2x. + In our implementation, we use a configurable parameter to set this threshold. + By default, we require the change in the log (base 10) of the performance improvement to be 2x the difference in the log of the space cost. + This works out to allowing a maximum 10x increase in space for a 100x performance improvement. Our implementation is most closely related to the [Anytime Algorithm](https://www.microsoft.com/en-us/research/wp-content/uploads/2020/06/Anytime-Algorithm-of-Database-Tuning-Advisor-for-Microsoft-SQL-Server.pdf) found in Microsoft SQL Server. Compared to [Dexter](https://github.com/ankane/dexter/), an automatic indexing tool for Postgres, we search a larger space and use different heuristics. @@ -470,20 +480,19 @@ This allows us to generate better solutions at the cost of longer runtime. We also show the work done in each round of the search, including a comparison of the query plans before and after the addition of each index. This give the LLM additional context that it can use when responding to the indexing recommendations. - ### Database Health Database health checks identify tuning opportunities and maintenance needs before they lead to critical issues. In the present release, Postgres MCP Pro adapts the database health checks directly from [PgHero](https://github.com/ankane/pghero). We are working to fully validate these checks and may extend them in the future. -- *Index Health*. Looks for unused indexes, duplicate indexes, and indexes that are bloated. Bloated indexes make inefficient use of database pages. +- _Index Health_. Looks for unused indexes, duplicate indexes, and indexes that are bloated. Bloated indexes make inefficient use of database pages. Postgres autovacuum cleans up index entries pointing to dead tuples, and marks the entries as reusable. However, it does not compact the index pages and, eventually, index pages may contain few live tuple references. -- *Buffer Cache Hit Rate*. Measures the proportion of database reads that are served from the buffer cache instead of disk. +- _Buffer Cache Hit Rate_. Measures the proportion of database reads that are served from the buffer cache instead of disk. A low buffer cache hit rate must be investigated as it is often not cost-optimal and leads to degraded application performance. -- *Connection Health*. Checks the number of connections to the database and reports on their utilization. +- _Connection Health_. Checks the number of connections to the database and reports on their utilization. The biggest risk is running out of connections, but a high number of idle or blocked connections can also indicate issues. -- *Vacuum Health*. Vacuum is important for many reasons. +- _Vacuum Health_. Vacuum is important for many reasons. A critical one is preventing transaction id wraparound, which can cause the database to stop accepting writes. The Postgres multi-version concurrency control (MVCC) mechanism requires a unique transaction id for each transaction. However, because Postgres uses a 32-bit signed integer for transaction ids, it needs to reuse transaction ids after after a maximum of 2 billion transactions. @@ -491,11 +500,10 @@ We are working to fully validate these checks and may extend them in the future. When records first go to disk, they are written visibility for a range of transaction ids. Before re-using these transaction ids, Postgres must update any on-disk records, "freezing" them to remove the references to the transaction ids to be reused. This check looks for tables that require vacuuming to prevent transaction id wraparound. -- *Replication Health*. Checks replication health by monitoring lag between primary and replicas, verifying replication status, and tracking usage of replication slots. -- *Constraint Health*. During normal operation, Postgres rejects any transactions that would cause a constraint violation. +- _Replication Health_. Checks replication health by monitoring lag between primary and replicas, verifying replication status, and tracking usage of replication slots. +- _Constraint Health_. During normal operation, Postgres rejects any transactions that would cause a constraint violation. However, invalid constraints may occur after loading data or in recovery scenarios. This check looks for any invalid constraints. -- *Sequence Health*. Looks for sequences that are at risk of exceeding their maximum value. - +- _Sequence Health_. Looks for sequences that are at risk of exceeding their maximum value. ### Postgres Client Library @@ -509,7 +517,6 @@ Asyncpg is also probably [faster](https://fernandoarteaga.dev/blog/psycopg-vs-as Balancing these considerations, we selected `psycopg3` over `asyncpg`. We remain open to revising this decision in the future. - ### Connection Configuration Like the [Reference PostgreSQL MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres), Postgres MCP Pro takes Postgres connection information at startup. @@ -522,27 +529,24 @@ There must be a better approach than either of these. Both have security weaknessesβ€”few MCP clients store the MCP server configuration securely (an exception is Goose), and credentials provided via MCP tools are passed through the LLM and stored in the chat history. Both also have usability issues in some scenarios. - ### Schema Information The purpose of the schema information tool is to provide the calling AI agent with the information it needs to generate correct and performant SQL. For example, suppose a user asks, "How many flights took off from San Francisco and landed in Paris during the past year?" The AI agent needs to find the table that stores the flights, the columns that store the origin and destinations, and perhaps a table that maps between airport codes and airport locations. - -*Why provide schema information tools when LLMs are generally capable of generating the SQL to retrieve this information from Postgres directly?* +_Why provide schema information tools when LLMs are generally capable of generating the SQL to retrieve this information from Postgres directly?_ Our experience using Claude indicates that the calling LLM is very good at generating SQL to explore the Postgres schema by querying the [Postgres system catalog](https://www.postgresql.org/docs/current/catalogs.html) and the [information schema](https://www.postgresql.org/docs/current/information-schema.html) (an ANSI-standardized database metadata view). However, we do not know whether other LLMs do so as reliably and capably. -*Would it be better to provide schema information using [MCP resources](https://modelcontextprotocol.io/docs/concepts/resources) rather than [MCP tools](https://modelcontextprotocol.io/docs/concepts/tools)?* +_Would it be better to provide schema information using [MCP resources](https://modelcontextprotocol.io/docs/concepts/resources) rather than [MCP tools](https://modelcontextprotocol.io/docs/concepts/tools)?_ The [Reference PostgreSQL MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres) uses resources to expose schema information rather than tools. Navigating resources is similar to navigating a file system, so this approach is natural in many ways. However, resource support is less widespread than tool support in the MCP client ecosystem (see [example clients](https://modelcontextprotocol.io/clients)). In addition, while the MCP standard says that resources can be accessed by either AI agents or end-user humans, some clients only support human navigation of the resource tree. - ### Protected SQL Execution AI amplifies longstanding challenges of protecting databases from a range of threats, ranging from simple mistakes to sophisticated attacks by malicious actors. @@ -564,6 +568,7 @@ To make this mechanism robust, we need to ensure that the SQL does not somehow c For example, the LLM can circumvent the read-only transaction mode by issuing a `ROLLBACK` statement and then beginning a new transaction. For example: + ```sql ROLLBACK; DROP TABLE users; ``` @@ -574,10 +579,11 @@ Helpfully, the popular Postgres stored procedure languages, including PL/pgSQL a If you have unsafe stored procedure languages enabled on your database, then our read-only protections could be circumvented. At present, Postgres MCP Pro provides two levels of protection for the database, one at either extreme of the convenience/safety spectrum. + - "Unrestricted" provides maximum flexibility. -It is suitable for development environments where speed and flexibility are paramount, and where there is no need to protect valuable or sensitive data. + It is suitable for development environments where speed and flexibility are paramount, and where there is no need to protect valuable or sensitive data. - "Restricted" provides a balance between flexibility and safety. -It is suitable for production environments where the database is exposed to untrusted users, and where it is important to protect valuable or sensitive data. + It is suitable for production environments where the database is exposed to untrusted users, and where it is important to protect valuable or sensitive data. Unrestricted mode aligns with the approach of [Cursor's auto-run mode](https://docs.cursor.com/chat/tools#auto-run), where the AI agent operates with limited human oversight or approvals. We expect auto-run to be deployed in development environments where the consequences of mistakes are low, where databases do not contain valuable or sensitive data, and where they can be recreated or restored from backups when needed. @@ -586,7 +592,6 @@ We designed restricted mode to be conservative, erring on the side of safety eve Restricted mode is limited to read-only operations, and we limit query execution time to prevent long-running queries from impacting system performance. We may add measures in the future to make sure that restricted mode is safe to use with production databases. - ## Postgres MCP Pro Development The instructions below are for developers who want to work on Postgres MCP Pro, or users who prefer to install Postgres MCP Pro from source. From b54c5da84692e083a7fbc31bb28870dc555359e5 Mon Sep 17 00:00:00 2001 From: Mark Valdez <1289198+valdezm@users.noreply.github.com> Date: Wed, 14 May 2025 11:52:44 -0700 Subject: [PATCH 02/18] Update README for WSL UVX usage, no formatting --- README.md | 252 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 132 insertions(+), 120 deletions(-) diff --git a/README.md b/README.md index 1cbe0b40..6cb874aa 100644 --- a/README.md +++ b/README.md @@ -42,13 +42,11 @@ For additional background on why we built Postgres MCP Pro, see [our launch blog ## Demo -_From Unusable to Lightning Fast_ - +*From Unusable to Lightning Fast* - **Challenge:** We generated a movie app using an AI assistant, but the SQLAlchemy ORM code ran painfully slow. - **Solution:** Using Postgres MCP Pro with Cursor, we fixed the performance issues in minutes. What we did: - - πŸš€ Fixed performance - including ORM queries, indexing, and caching - πŸ› οΈ Fixed a broken page - by prompting the agent to explore the data, fix queries, and add related content. - 🧠 Improved the top movies - by exploring the data and fixing the ORM query to surface more relevant results. @@ -57,18 +55,20 @@ See the video below or read the [play-by-play](examples/movie-app.md). https://github.com/user-attachments/assets/24e05745-65e9-4998-b877-a368f1eadc13 + + + ## Quick Start ### Prerequisites Before getting started, ensure you have: - 1. Access credentials for your database. -2. Docker _or_ Python 3.12 or higher. +2. Docker *or* Python 3.12 or higher. #### Access Credentials + You can confirm your access credentials are valid by using `psql` or a GUI tool such as [pgAdmin](https://www.pgadmin.org/). -You can confirm your access credentials are valid by using `psql` or a GUI tool such as [pgAdmin](https://www.pgadmin.org/). #### Docker or Python @@ -76,6 +76,7 @@ The choice to use Docker or Python is yours. We generally recommend Docker because Python users can encounter more environment-specific issues. However, it often makes sense to use whichever method you are most familiar with. + ### Installation Choose one of the following methods to install Postgres MCP Pro: @@ -89,6 +90,7 @@ This image contains all necessary dependencies, providing a reliable way to run docker pull crystaldba/postgres-mcp ``` + #### Option 2: Using Python If you have `pipx` installed you can install Postgres MCP Pro with: @@ -105,6 +107,7 @@ uv pip install postgres-mcp If you need to install `uv`, see the [uv installation instructions](https://docs.astral.sh/uv/getting-started/installation/). + ### Configure Your AI Assistant We provide full instructions for configuring Postgres MCP Pro with Claude Desktop. @@ -114,7 +117,6 @@ Many MCP clients have similar configuration files, you can adapt these steps to You will need to edit the Claude Desktop configuration file to add Postgres MCP Pro. The location of this file depends on your operating system: - - MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json` - Windows: `%APPDATA%/Claude/claude_desktop_config.json` @@ -168,6 +170,7 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho - MacOS/Windows: Uses `host.docker.internal` automatically - Linux: Uses `172.17.0.1` or the appropriate host address automatically + ##### If you are using `pipx` ```json @@ -175,7 +178,9 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho "mcpServers": { "postgres": { "command": "postgres-mcp", - "args": ["--access-mode=unrestricted"], + "args": [ + "--access-mode=unrestricted" + ], "env": { "DATABASE_URI": "postgresql://username:password@localhost:5432/dbname" } @@ -184,6 +189,7 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho } ``` + ##### If you are using `uv` ```json @@ -191,7 +197,11 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho "mcpServers": { "postgres": { "command": "uv", - "args": ["run", "postgres-mcp", "--access-mode=unrestricted"], + "args": [ + "run", + "postgres-mcp", + "--access-mode=unrestricted" + ], "env": { "DATABASE_URI": "postgresql://username:password@localhost:5432/dbname" } @@ -200,19 +210,21 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho } ``` + ##### Connection URI Replace `postgresql://...` with your [Postgres database connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS). -##### Access Mode -Postgres MCP Pro supports multiple _access modes_ to give you control over the operations that the AI agent can perform on the database: +##### Access Mode +Postgres MCP Pro supports multiple *access modes* to give you control over the operations that the AI agent can perform on the database: - **Unrestricted Mode**: Allows full read/write access to modify data and schema. It is suitable for development environments. - **Restricted Mode**: Limits operations to read-only transactions and imposes constraints on resource utilization (presently only execution time). It is suitable for production environments. To use restricted mode, replace `--access-mode=unrestricted` with `--access-mode=restricted` in the configuration examples above. + #### Other MCP Clients Many MCP clients have similar configuration files to Claude Desktop, and you can adapt the examples above to work with the client of your choice. @@ -239,12 +251,12 @@ For example, in Cursor's `mcp.json` or Cline's `cline_mcp_settings.json` you can ```json { - "mcpServers": { - "postgres": { - "type": "sse", - "url": "http://localhost:8000/sse" + "mcpServers": { + "postgres": { + "type": "sse", + "url": "http://localhost:8000/sse" + } } - } } ``` @@ -252,12 +264,12 @@ For Windsurf, the format in `mcp_config.json` is slightly different: ```json { - "mcpServers": { - "postgres": { - "type": "sse", - "serverUrl": "http://localhost:8000/sse" + "mcpServers": { + "postgres": { + "type": "sse", + "serverUrl": "http://localhost:8000/sse" + } } - } } ``` @@ -266,7 +278,7 @@ For Windsurf, the format in `mcp_config.json` is slightly different: To enable index tuning and comprehensive performance analysis you need to load the `pg_statements` and `hypopg` extensions on your database. - The `pg_statements` extension allows Postgres MCP Pro to analyze query execution statistics. - For example, this allows it to understand which queries are running slow or consuming significant resources. +For example, this allows it to understand which queries are running slow or consuming significant resources. - The `hypopg` extension allows Postgres MCP Pro to simulate the behavior of the Postgres query planner after adding indexes. ### Installing extensions on AWS RDS, Azure SQL, or Google Cloud SQL @@ -290,31 +302,26 @@ The `hypopg` extension may also require additional system-level installation (e. ### Get Database Health Overview Ask: - > Check the health of my database and identify any issues. ### Analyze Slow Queries Ask: - > What are the slowest queries in my database? And how can I speed them up? ### Get Recommendations On How To Speed Things Up Ask: - > My app is slow. How can I make it faster? ### Generate Index Recommendations Ask: - > Analyze my database workload and suggest indexes to improve performance. ### Optimize a Specific Query Ask: - > Help me optimize this query: SELECT \* FROM orders JOIN customers ON orders.customer_id = customers.id WHERE orders.created_at > '2023-01-01'; ## MCP Server API @@ -325,24 +332,25 @@ Postgres MCP Pro provides functionality via [MCP tools](https://modelcontextprot We chose this approach because the [MCP client ecosystem](https://modelcontextprotocol.io/clients) has widespread support for MCP tools. This contrasts with the approach of other Postgres MCP servers, including the [Reference Postgres MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres), which use [MCP resources](https://modelcontextprotocol.io/docs/concepts/resources) to expose schema information. + Postgres MCP Pro Tools: -| Tool Name | Description | -| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `list_schemas` | Lists all database schemas available in the PostgreSQL instance. | -| `list_objects` | Lists database objects (tables, views, sequences, extensions) within a specified schema. | -| `get_object_details` | Provides information about a specific database object, for example, a table's columns, constraints, and indexes. | -| `execute_sql` | Executes SQL statements on the database, with read-only limitations when connected in restricted mode. | -| `explain_query` | Gets the execution plan for a SQL query describing how PostgreSQL will process it and exposing the query planner's cost model. Can be invoked with hypothetical indexes to simulate the behavior after adding indexes. | -| `get_top_queries` | Reports the slowest SQL queries based on total execution time using `pg_stat_statements` data. | -| `analyze_workload_indexes` | Analyzes the database workload to identify resource-intensive queries, then recommends optimal indexes for them. | -| `analyze_query_indexes` | Analyzes a list of specific SQL queries (up to 10) and recommends optimal indexes for them. | -| `analyze_db_health` | Performs comprehensive health checks including: buffer cache hit rates, connection health, constraint validation, index health (duplicate/unused/invalid), sequence limits, and vacuum health. | +| Tool Name | Description | +|-----------|-------------| +| `list_schemas` | Lists all database schemas available in the PostgreSQL instance. | +| `list_objects` | Lists database objects (tables, views, sequences, extensions) within a specified schema. | +| `get_object_details` | Provides information about a specific database object, for example, a table's columns, constraints, and indexes. | +| `execute_sql` | Executes SQL statements on the database, with read-only limitations when connected in restricted mode. | +| `explain_query` | Gets the execution plan for a SQL query describing how PostgreSQL will process it and exposing the query planner's cost model. Can be invoked with hypothetical indexes to simulate the behavior after adding indexes. | +| `get_top_queries` | Reports the slowest SQL queries based on total execution time using `pg_stat_statements` data. | +| `analyze_workload_indexes` | Analyzes the database workload to identify resource-intensive queries, then recommends optimal indexes for them. | +| `analyze_query_indexes` | Analyzes a list of specific SQL queries (up to 10) and recommends optimal indexes for them. | +| `analyze_db_health` | Performs comprehensive health checks including: buffer cache hit rates, connection health, constraint validation, index health (duplicate/unused/invalid), sequence limits, and vacuum health. | + ## Related Projects **Postgres MCP Servers** - - [Query MCP](https://github.com/alexander-zuev/supabase-mcp-server). An MCP server for Supabase Postgres with a three-tier safety architecture and Supabase management API support. - [PG-MCP](https://github.com/stuzero/pg-mcp-server). An MCP server for PostgreSQL with flexible connection options, explain plans, extension context, and more. - [Reference PostgreSQL MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres). A simple MCP Server implementation exposing schema information as MCP resources and executing read-only queries. @@ -352,7 +360,6 @@ Postgres MCP Pro Tools: - [Wren MCP Server](https://github.com/Canner/wren-engine). Provides a semantic engine powering business intelligence for Postgres and other databases. **DBA Tools (including commercial offerings)** - - [Aiven Database Optimizer](https://aiven.io/solutions/aiven-ai-database-optimizer). A tool that provides holistic database workload analysis, query optimizations, and other performance improvements. - [dba.ai](https://www.dba.ai/). An AI-powered database administration assistant that integrates with GitHub to resolve code issues. - [pgAnalyze](https://pganalyze.com/). A comprehensive monitoring and analytics platform for identifying performance bottlenecks, optimizing queries, and real-time alerting. @@ -360,49 +367,49 @@ Postgres MCP Pro Tools: - [Xata Agent](https://github.com/xataio/agent). An open-source AI agent that automatically monitors database health, diagnoses issues, and provides recommendations using LLM-powered reasoning and playbooks. **Postgres Utilities** - - [Dexter](https://github.com/DexterDB/dexter). A tool for generating and testing hypothetical indexes on PostgreSQL. - [PgHero](https://github.com/ankane/pghero). A performance dashboard for Postgres, with recommendations. - Postgres MCP Pro incorporates health checks from PgHero. +Postgres MCP Pro incorporates health checks from PgHero. - [PgTune](https://github.com/le0pard/pgtune?tab=readme-ov-file). Heuristics for tuning Postgres configuration. ## Frequently Asked Questions -_How is Postgres MCP Pro different from other Postgres MCP servers?_ +*How is Postgres MCP Pro different from other Postgres MCP servers?* There are many MCP servers allow an AI agent to run queries against a Postgres database. Postgres MCP Pro does that too, but also adds tools for understanding and improving the performance of your Postgres database. For example, it implements a version of the [Anytime Algorithm of Database Tuning Advisor for Microsoft SQL Server](https://www.microsoft.com/en-us/research/wp-content/uploads/2020/06/Anytime-Algorithm-of-Database-Tuning-Advisor-for-Microsoft-SQL-Server.pdf), a modern industrial-strength algorithm for automatic index tuning. -| Postgres MCP Pro | Other Postgres MCP Servers | -| ----------------------------------------- | -------------------------------------------- | -| βœ… Deterministic database health checks | ❌ Unrepeatable LLM-generated health queries | -| βœ… Principled indexing search strategies | ❌ Gen-AI guesses at indexing improvements | -| βœ… Workload analysis to find top problems | ❌ Inconsistent problem analysis | -| βœ… Simulates performance improvements | ❌ Try it yourself and see if it works | +| Postgres MCP Pro | Other Postgres MCP Servers | +|--------------|----------------------------| +| βœ… Deterministic database health checks | ❌ Unrepeatable LLM-generated health queries | +| βœ… Principled indexing search strategies | ❌ Gen-AI guesses at indexing improvements | +| βœ… Workload analysis to find top problems | ❌ Inconsistent problem analysis | +| βœ… Simulates performance improvements | ❌ Try it yourself and see if it works | Postgres MCP Pro complements generative AI by adding deterministic tools and classical optimization algorithms The combination is both reliable and flexible. -_Why are MCP tools needed when the LLM can reason, generate SQL, etc?_ + +*Why are MCP tools needed when the LLM can reason, generate SQL, etc?* LLMs are invaluable for tasks that involve ambiguity, reasoning, or natural language. When compared to procedural code, however, they can be slow, expensive, non-deterministic, and sometimes produce unreliable results. In the case of database tuning, we have well established algorithms, developed over decades, that are proven to work. Postgres MCP Pro lets you combine the best of both worlds by pairing LLMs with classical optimization algorithms and other procedural tools. -_How do you test Postgres MCP Pro?_ +*How do you test Postgres MCP Pro?* Testing is critical to ensuring that Postgres MCP Pro is reliable and accurate. We are building out a suite of AI-generated adversarial workloads designed to challenge Postgres MCP Pro and ensure it performs under a broad variety of scenarios. -_What Postgres versions are supported?_ +*What Postgres versions are supported?* Our testing presently focuses on Postgres 15, 16, and 17. We plan to support Postgres versions 13 through 17. -_Who created this project?_ +*Who created this project?* This project is created and maintained by [Crystal DBA](https://www.crystaldba.ai). ## Roadmap -_TBD_ +*TBD* You and your needs are a critical driver for what we build. Tell us what you want to see by opening an [issue](https://github.com/crystaldba/postgres-mcp/issues) or a [pull request](https://github.com/crystaldba/postgres-mcp/pulls). @@ -416,62 +423,62 @@ This section includes a high-level overview technical considerations that influe Developers know that missing indexes are one of the most common causes of database performance issues. Indexes provide access methods that allow Postgres to quickly locate data that is required to execute a query. -When tables are small, indexes make little difference, but as the size of the data grows, the difference in algorithmic complexity between a table scan and an index lookup becomes significant (typically _O_(_n_) vs _O_(_log_ _n_), potentially more if joins on multiple tables are involved). +When tables are small, indexes make little difference, but as the size of the data grows, the difference in algorithmic complexity between a table scan and an index lookup becomes significant (typically *O*(*n*) vs *O*(*log* *n*), potentially more if joins on multiple tables are involved). Generating suggested indexes in Postgres MCP Pro proceeds in several stages: -1. _Identify SQL queries in need of tuning_. - If you know you are having a problem with a specific SQL query you can provide it. - Postgres MCP Pro can also analyze the workload to identify index tuning targets. - To do this, it relies on the `pg_stat_statements` extension, which records the runtime and resource consumption of each query. - - A query is a candidate for index tuning if it is a top resource consumer, either on a per-execution basis or in aggregate. - At present, we use execution time as a proxy for cumulative resource consumption, but it may also make sense to look at specifics resources, e.g., the number of blocks accessed or the number of blocks read from disk. - The `analyze_query_workload` tool focuses on slow queries, using the mean time per execution with thresholds for execution count and mean execution time. - Agents may also call `get_top_queries`, which accepts a parameter for mean vs. total execution time, then pass these queries `analyze_query_indexes` to get index recommendations. - - Sophisticated index tuning systems use "workload compression" to produce a representative subset of queries that reflects the characteristics of the workload as a whole, reducing the problem for downstream algorithms. - Postgres MCP Pro performs a limited form of workload compression by normalizing queries so that those generated from the same template appear as one. - It weights each query equally, a simplification that works when the benefits to indexing are large. - -2. _Generate candidate indexes_ - Once we have a list of SQL queries that we want to improve through indexing, we generate a list of indexes that we might want to add. - To do this, we parse the SQL and identify any columns used in filters, joins, grouping, or sorting. - - To generate all possible indexes we need to consider combinations of these columns, because Postgres supports [multicolumn indexes](https://www.postgresql.org/docs/current/indexes-multicolumn.html). - In the present implementation, we include only one permutation of each possible multicolumn index, which is selected at random. - We make this simplification to reduce the search space because permutations often have equivalent performance. - However, we hope to improve in this area. - -3. _Search for the optimal index configuration_. - Our objective is to find the combination of indexes that optimally balances the performance benefits against the costs of storing and maintaining those indexes. - We estimate the performance improvement by using the "what if?" capabilities provided by the `hypopg` extension. - This simulates how the Postgres query optimizer will execute a query after the addition of indexes, and reports changes based on the actual Postgres cost model. - - One challenge is that generating query plans generally requires knowledge of the specific parameter values used in the query. - Query normalization, which is necessary to reduce the queries under consideration, removes parameter constants. - Parameter values provided via bind variables are similarly not available to us. - - To address this problem, we produce realistic constants that we can provide as parameters by sampling from the table statistics. - In version 16, Postgres added [generic explain plan functionality](https://www.postgresql.org/docs/current/sql-explain.html), but it has limitations, for example around `LIKE` clauses, which our implementation does not have. - - Search strategy is critical because evaluating all possible index combinations feasible only in simple situations. - This is what most sets apart various indexing approaches. - Adapting the approach of Microsoft's Anytime algorithm, we employ a greedy search strategy, i.e., find the best one-index solution, then find the best index to add to that to produce a two-index solution. - Our search terminates when the time budget is exhausted or when a round of exploration fails to produce any gains above the minimum improvement threshold of 10%. - -4. _Cost-benefit analysis_. - When posed with two indexing alternatives, one which produces better performance and one which requires more space, how do we decide which to choose? - Traditionally, index advisors ask for a storage budget and optimize performance with respect to that storage budget. - We also take a storage budget, but perform a cost-benefit analysis throughout the optimization. - - We frame this as the problem of selecting a point along the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front)β€”the set of choices for which improving one quality metric necessarily worsens another. - In an ideal world, we might want to assess the cost of the storage and the benefit of improved performance in monetary terms. - However, there is a simpler and more practical approach: to look at the changes in relative terms. - Most people would agree that a 100x performance improvement is worth it, even if the storage cost is 2x. - In our implementation, we use a configurable parameter to set this threshold. - By default, we require the change in the log (base 10) of the performance improvement to be 2x the difference in the log of the space cost. - This works out to allowing a maximum 10x increase in space for a 100x performance improvement. +1. *Identify SQL queries in need of tuning*. + If you know you are having a problem with a specific SQL query you can provide it. + Postgres MCP Pro can also analyze the workload to identify index tuning targets. + To do this, it relies on the `pg_stat_statements` extension, which records the runtime and resource consumption of each query. + + A query is a candidate for index tuning if it is a top resource consumer, either on a per-execution basis or in aggregate. + At present, we use execution time as a proxy for cumulative resource consumption, but it may also make sense to look at specifics resources, e.g., the number of blocks accessed or the number of blocks read from disk. + The `analyze_query_workload` tool focuses on slow queries, using the mean time per execution with thresholds for execution count and mean execution time. + Agents may also call `get_top_queries`, which accepts a parameter for mean vs. total execution time, then pass these queries `analyze_query_indexes` to get index recommendations. + + Sophisticated index tuning systems use "workload compression" to produce a representative subset of queries that reflects the characteristics of the workload as a whole, reducing the problem for downstream algorithms. + Postgres MCP Pro performs a limited form of workload compression by normalizing queries so that those generated from the same template appear as one. + It weights each query equally, a simplification that works when the benefits to indexing are large. + +2. *Generate candidate indexes* + Once we have a list of SQL queries that we want to improve through indexing, we generate a list of indexes that we might want to add. + To do this, we parse the SQL and identify any columns used in filters, joins, grouping, or sorting. + + To generate all possible indexes we need to consider combinations of these columns, because Postgres supports [multicolumn indexes](https://www.postgresql.org/docs/current/indexes-multicolumn.html). + In the present implementation, we include only one permutation of each possible multicolumn index, which is selected at random. + We make this simplification to reduce the search space because permutations often have equivalent performance. + However, we hope to improve in this area. + +3. *Search for the optimal index configuration*. + Our objective is to find the combination of indexes that optimally balances the performance benefits against the costs of storing and maintaining those indexes. + We estimate the performance improvement by using the "what if?" capabilities provided by the `hypopg` extension. + This simulates how the Postgres query optimizer will execute a query after the addition of indexes, and reports changes based on the actual Postgres cost model. + + One challenge is that generating query plans generally requires knowledge of the specific parameter values used in the query. + Query normalization, which is necessary to reduce the queries under consideration, removes parameter constants. + Parameter values provided via bind variables are similarly not available to us. + + To address this problem, we produce realistic constants that we can provide as parameters by sampling from the table statistics. + In version 16, Postgres added [generic explain plan functionality](https://www.postgresql.org/docs/current/sql-explain.html), but it has limitations, for example around `LIKE` clauses, which our implementation does not have. + + Search strategy is critical because evaluating all possible index combinations feasible only in simple situations. + This is what most sets apart various indexing approaches. + Adapting the approach of Microsoft's Anytime algorithm, we employ a greedy search strategy, i.e., find the best one-index solution, then find the best index to add to that to produce a two-index solution. + Our search terminates when the time budget is exhausted or when a round of exploration fails to produce any gains above the minimum improvement threshold of 10%. + +4. *Cost-benefit analysis*. + When posed with two indexing alternatives, one which produces better performance and one which requires more space, how do we decide which to choose? + Traditionally, index advisors ask for a storage budget and optimize performance with respect to that storage budget. + We also take a storage budget, but perform a cost-benefit analysis throughout the optimization. + + We frame this as the problem of selecting a point along the [Pareto front](https://en.wikipedia.org/wiki/Pareto_front)β€”the set of choices for which improving one quality metric necessarily worsens another. + In an ideal world, we might want to assess the cost of the storage and the benefit of improved performance in monetary terms. + However, there is a simpler and more practical approach: to look at the changes in relative terms. + Most people would agree that a 100x performance improvement is worth it, even if the storage cost is 2x. + In our implementation, we use a configurable parameter to set this threshold. + By default, we require the change in the log (base 10) of the performance improvement to be 2x the difference in the log of the space cost. + This works out to allowing a maximum 10x increase in space for a 100x performance improvement. Our implementation is most closely related to the [Anytime Algorithm](https://www.microsoft.com/en-us/research/wp-content/uploads/2020/06/Anytime-Algorithm-of-Database-Tuning-Advisor-for-Microsoft-SQL-Server.pdf) found in Microsoft SQL Server. Compared to [Dexter](https://github.com/ankane/dexter/), an automatic indexing tool for Postgres, we search a larger space and use different heuristics. @@ -480,19 +487,20 @@ This allows us to generate better solutions at the cost of longer runtime. We also show the work done in each round of the search, including a comparison of the query plans before and after the addition of each index. This give the LLM additional context that it can use when responding to the indexing recommendations. + ### Database Health Database health checks identify tuning opportunities and maintenance needs before they lead to critical issues. In the present release, Postgres MCP Pro adapts the database health checks directly from [PgHero](https://github.com/ankane/pghero). We are working to fully validate these checks and may extend them in the future. -- _Index Health_. Looks for unused indexes, duplicate indexes, and indexes that are bloated. Bloated indexes make inefficient use of database pages. +- *Index Health*. Looks for unused indexes, duplicate indexes, and indexes that are bloated. Bloated indexes make inefficient use of database pages. Postgres autovacuum cleans up index entries pointing to dead tuples, and marks the entries as reusable. However, it does not compact the index pages and, eventually, index pages may contain few live tuple references. -- _Buffer Cache Hit Rate_. Measures the proportion of database reads that are served from the buffer cache instead of disk. +- *Buffer Cache Hit Rate*. Measures the proportion of database reads that are served from the buffer cache instead of disk. A low buffer cache hit rate must be investigated as it is often not cost-optimal and leads to degraded application performance. -- _Connection Health_. Checks the number of connections to the database and reports on their utilization. +- *Connection Health*. Checks the number of connections to the database and reports on their utilization. The biggest risk is running out of connections, but a high number of idle or blocked connections can also indicate issues. -- _Vacuum Health_. Vacuum is important for many reasons. +- *Vacuum Health*. Vacuum is important for many reasons. A critical one is preventing transaction id wraparound, which can cause the database to stop accepting writes. The Postgres multi-version concurrency control (MVCC) mechanism requires a unique transaction id for each transaction. However, because Postgres uses a 32-bit signed integer for transaction ids, it needs to reuse transaction ids after after a maximum of 2 billion transactions. @@ -500,10 +508,11 @@ We are working to fully validate these checks and may extend them in the future. When records first go to disk, they are written visibility for a range of transaction ids. Before re-using these transaction ids, Postgres must update any on-disk records, "freezing" them to remove the references to the transaction ids to be reused. This check looks for tables that require vacuuming to prevent transaction id wraparound. -- _Replication Health_. Checks replication health by monitoring lag between primary and replicas, verifying replication status, and tracking usage of replication slots. -- _Constraint Health_. During normal operation, Postgres rejects any transactions that would cause a constraint violation. +- *Replication Health*. Checks replication health by monitoring lag between primary and replicas, verifying replication status, and tracking usage of replication slots. +- *Constraint Health*. During normal operation, Postgres rejects any transactions that would cause a constraint violation. However, invalid constraints may occur after loading data or in recovery scenarios. This check looks for any invalid constraints. -- _Sequence Health_. Looks for sequences that are at risk of exceeding their maximum value. +- *Sequence Health*. Looks for sequences that are at risk of exceeding their maximum value. + ### Postgres Client Library @@ -517,6 +526,7 @@ Asyncpg is also probably [faster](https://fernandoarteaga.dev/blog/psycopg-vs-as Balancing these considerations, we selected `psycopg3` over `asyncpg`. We remain open to revising this decision in the future. + ### Connection Configuration Like the [Reference PostgreSQL MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres), Postgres MCP Pro takes Postgres connection information at startup. @@ -529,24 +539,27 @@ There must be a better approach than either of these. Both have security weaknessesβ€”few MCP clients store the MCP server configuration securely (an exception is Goose), and credentials provided via MCP tools are passed through the LLM and stored in the chat history. Both also have usability issues in some scenarios. + ### Schema Information The purpose of the schema information tool is to provide the calling AI agent with the information it needs to generate correct and performant SQL. For example, suppose a user asks, "How many flights took off from San Francisco and landed in Paris during the past year?" The AI agent needs to find the table that stores the flights, the columns that store the origin and destinations, and perhaps a table that maps between airport codes and airport locations. -_Why provide schema information tools when LLMs are generally capable of generating the SQL to retrieve this information from Postgres directly?_ + +*Why provide schema information tools when LLMs are generally capable of generating the SQL to retrieve this information from Postgres directly?* Our experience using Claude indicates that the calling LLM is very good at generating SQL to explore the Postgres schema by querying the [Postgres system catalog](https://www.postgresql.org/docs/current/catalogs.html) and the [information schema](https://www.postgresql.org/docs/current/information-schema.html) (an ANSI-standardized database metadata view). However, we do not know whether other LLMs do so as reliably and capably. -_Would it be better to provide schema information using [MCP resources](https://modelcontextprotocol.io/docs/concepts/resources) rather than [MCP tools](https://modelcontextprotocol.io/docs/concepts/tools)?_ +*Would it be better to provide schema information using [MCP resources](https://modelcontextprotocol.io/docs/concepts/resources) rather than [MCP tools](https://modelcontextprotocol.io/docs/concepts/tools)?* The [Reference PostgreSQL MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/postgres) uses resources to expose schema information rather than tools. Navigating resources is similar to navigating a file system, so this approach is natural in many ways. However, resource support is less widespread than tool support in the MCP client ecosystem (see [example clients](https://modelcontextprotocol.io/clients)). In addition, while the MCP standard says that resources can be accessed by either AI agents or end-user humans, some clients only support human navigation of the resource tree. + ### Protected SQL Execution AI amplifies longstanding challenges of protecting databases from a range of threats, ranging from simple mistakes to sophisticated attacks by malicious actors. @@ -568,7 +581,6 @@ To make this mechanism robust, we need to ensure that the SQL does not somehow c For example, the LLM can circumvent the read-only transaction mode by issuing a `ROLLBACK` statement and then beginning a new transaction. For example: - ```sql ROLLBACK; DROP TABLE users; ``` @@ -579,11 +591,10 @@ Helpfully, the popular Postgres stored procedure languages, including PL/pgSQL a If you have unsafe stored procedure languages enabled on your database, then our read-only protections could be circumvented. At present, Postgres MCP Pro provides two levels of protection for the database, one at either extreme of the convenience/safety spectrum. - - "Unrestricted" provides maximum flexibility. - It is suitable for development environments where speed and flexibility are paramount, and where there is no need to protect valuable or sensitive data. +It is suitable for development environments where speed and flexibility are paramount, and where there is no need to protect valuable or sensitive data. - "Restricted" provides a balance between flexibility and safety. - It is suitable for production environments where the database is exposed to untrusted users, and where it is important to protect valuable or sensitive data. +It is suitable for production environments where the database is exposed to untrusted users, and where it is important to protect valuable or sensitive data. Unrestricted mode aligns with the approach of [Cursor's auto-run mode](https://docs.cursor.com/chat/tools#auto-run), where the AI agent operates with limited human oversight or approvals. We expect auto-run to be deployed in development environments where the consequences of mistakes are low, where databases do not contain valuable or sensitive data, and where they can be recreated or restored from backups when needed. @@ -592,6 +603,7 @@ We designed restricted mode to be conservative, erring on the side of safety eve Restricted mode is limited to read-only operations, and we limit query execution time to prevent long-running queries from impacting system performance. We may add measures in the future to make sure that restricted mode is safe to use with production databases. + ## Postgres MCP Pro Development The instructions below are for developers who want to work on Postgres MCP Pro, or users who prefer to install Postgres MCP Pro from source. From a524b929dce2bcce5cb0da8bc76f3cd9d787cc5a Mon Sep 17 00:00:00 2001 From: Mark Valdez <1289198+valdezm@users.noreply.github.com> Date: Wed, 14 May 2025 11:56:33 -0700 Subject: [PATCH 03/18] more info, clearer --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 6cb874aa..c54454bc 100644 --- a/README.md +++ b/README.md @@ -134,12 +134,15 @@ You will now edit the `mcpServers` section of the configuration file. "args": [ "bash", "-c", - "/home/mark/.local/bin/uvx --from git+https://github.com/crystaldba/postgres-mcp@main postgres-mcp postgresql://username:password@localhost:5432/dbname --access-mode=unrestricted" + "/home/$WSL_USER/.local/bin/uvx --from git+https://github.com/crystaldba/postgres-mcp@main postgres-mcp postgresql://username:password@localhost:5432/dbname --access-mode=unrestricted" ] } } } ``` +_Replace `$WSL_USER` with your WSL username_ +_If you do not know where uvx is installed, run `which uvx` in WSL_ +_If you do not have uvx installed, see section above for instructions_ ##### If you are using Docker From caa0c88ad169d4daba5fd6a8d2e27975f570577d Mon Sep 17 00:00:00 2001 From: Johann Schleier-Smith Date: Thu, 15 May 2025 10:56:58 -0700 Subject: [PATCH 04/18] edited the instructions --- README.md | 44 ++++++++++++++++++++++++-------------------- 1 file changed, 24 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index c54454bc..3a5e8a80 100644 --- a/README.md +++ b/README.md @@ -124,26 +124,6 @@ You can also use `Settings` menu item in Claude Desktop to locate the configurat You will now edit the `mcpServers` section of the configuration file. -#### Windows WSL(using UVX, no need to install anything) - -```json -{ - "mcpServers": { - "postgres": { - "command": "wsl.exe", - "args": [ - "bash", - "-c", - "/home/$WSL_USER/.local/bin/uvx --from git+https://github.com/crystaldba/postgres-mcp@main postgres-mcp postgresql://username:password@localhost:5432/dbname --access-mode=unrestricted" - ] - } - } -} -``` -_Replace `$WSL_USER` with your WSL username_ -_If you do not know where uvx is installed, run `which uvx` in WSL_ -_If you do not have uvx installed, see section above for instructions_ - ##### If you are using Docker ```json @@ -213,6 +193,30 @@ The Postgres MCP Pro Docker image will automatically remap the hostname `localho } ``` +##### If you are using `uvx` with Windows WSL + +If you are using Windows WSL, you can use `uvx` with the following configuration to download and run Postgres MCP Pro: + +```json +{ + "mcpServers": { + "postgres": { + "command": "wsl.exe", + "args": [ + "bash", + "-c", + "/home/$WSL_USER/.local/bin/uvx --from git+https://github.com/crystaldba/postgres-mcp@main postgres-mcp postgresql://username:password@localhost:5432/dbname --access-mode=unrestricted" + ] + } + } +} +``` + +Notes: +- This command runs the latest version of Postgres MCP Pro from the `main` branch. +- Replace `/home/$WSL_USER/.local/bin/uvx` with the path to your `uvx` command. If you do not know where `uvx` is installed, run `which uvx` in WSL. +- If you do not have `uvx` installed, you may install it using `pip`. + ##### Connection URI From 26081332ab5e20fd519da914a14104464a1a1e1f Mon Sep 17 00:00:00 2001 From: Henning Berge Date: Fri, 30 Jan 2026 10:01:25 +0100 Subject: [PATCH 05/18] Return error message if table does not exist --- src/postgres_mcp/server.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/src/postgres_mcp/server.py b/src/postgres_mcp/server.py index f3ba8f8b..0261e2c3 100644 --- a/src/postgres_mcp/server.py +++ b/src/postgres_mcp/server.py @@ -215,6 +215,22 @@ async def get_object_details( """, [schema_name, object_name], ) + + # If no columns found, check if table/view actually exists + if not col_rows: + exists_rows = await SafeSqlDriver.execute_param_query( + sql_driver, + """ + SELECT tablename FROM pg_tables WHERE schemaname = {} AND tablename = {} + UNION + SELECT viewname FROM pg_views WHERE schemaname = {} AND viewname = {} + """, + [schema_name, object_name, schema_name, object_name], + ) + + if not exists_rows: + return format_error_response(f"{object_type.capitalize()} '{schema_name}.{object_name}' does not exist") + columns = ( [ { From 526109576f9fb87e392c5038486764859f6c82f5 Mon Sep 17 00:00:00 2001 From: Dev-iL <6509619+Dev-iL@users.noreply.github.com> Date: Thu, 19 Mar 2026 12:50:45 +0200 Subject: [PATCH 06/18] docs: Add AGENTS.md and CLAUDE.md for AI agent contribution guidance MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit AGENTS.md provides universal rules for all AI agents: safety guardrails (get_sql_driver() hard rule), CI commands, project structure, and code conventions. CLAUDE.md adds Claude-specific tool preferences, MCP-aware validation workflows, and commit style guidance β€” referencing AGENTS.md for shared rules rather than duplicating them. Co-Authored-By: Claude Opus 4.6 (1M context) --- AGENTS.md | 80 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ CLAUDE.md | 60 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 140 insertions(+) create mode 100644 AGENTS.md create mode 100644 CLAUDE.md diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 00000000..18a867b8 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,80 @@ +# AGENTS.md β€” postgres-mcp + +Universal instructions for AI agents contributing to this project. + +**Scope:** Development conventions, CI commands, and safety rules for the postgres-mcp codebase. Does NOT cover how to use the MCP tools as an end user (see `.claude/skills/postgres-mcp-usage.md` for that). + +## Safety Guardrails + +**Hard rule: every new tool MUST call `get_sql_driver()` to obtain its SQL driver.** This is the only mechanism that enforces the server's access mode (UNRESTRICTED vs RESTRICTED). Bypassing it β€” by instantiating `SqlDriver` directly β€” silently disables safety protections. + +- **UNRESTRICTED mode** β€” full read/write SQL access. Intended for development. +- **RESTRICTED mode** β€” read-only transactions via `SafeSqlDriver`, 30-second query timeout. Intended for production. +- `get_sql_driver()` returns a `SqlDriver` or `SafeSqlDriver` depending on `current_access_mode`. +- `execute_sql` is dynamically registered with different MCP annotations per mode. Follow this pattern for any new tool whose behavior changes with access mode. + +## CI Commands + +Run these locally before pushing. They mirror `.github/workflows/build.yml`: + +```bash +uv sync # install dependencies +uv run ruff format --check . # format check +uv run ruff check . # lint +uv run pyright # type check +uv run pytest -v --log-cli-level=INFO # tests (unit + integration) +``` + +All five must pass for CI to be green. + +## Project Structure + +``` +src/postgres_mcp/ + server.py # MCP server entry point, tool definitions (~700 lines) + sql/ # SQL driver, SafeSqlDriver, parameterized queries, extensions + index/ # Index tuning (DTA algorithm, LLM optimizer) + explain/ # EXPLAIN plan analysis + database_health/ # Health checks (index, connection, vacuum, sequence, replication, buffer, constraint) + top_queries/ # pg_stat_statements query analysis + artifacts.py # Response types (ExplainPlanArtifact, ErrorResult) + +tests/ + unit/ # Fast, no database required + integration/ # Requires a running PostgreSQL instance (Docker) + conftest.py # Shared fixtures +``` + +Key entry point: `server.py` β€” contains all `@mcp.tool` definitions and the `main()` function. + +## Code Conventions + +See `pyproject.toml` for full ruff/pyright configuration. Key points: + +- **Async throughout** β€” all tool handlers and SQL operations are `async`. Use `await` for database calls. +- **Line length:** 150 characters. +- **Quotes:** double quotes (ruff format). +- **Imports:** force-single-line (`from x import y`, one per line). Enforced by ruff isort. +- **Type checking:** pyright in standard mode, Python 3.12. Ruff lint targets Python 3.9 for compatibility. +- **Lint rules:** E, F, I, B, W, N, UP, RUF. See `pyproject.toml [tool.ruff]` for active ignores. + +### Patterns to Follow + +- Use `Field(description=..., default=...)` from pydantic for tool parameter definitions. +- Return `ResponseType` (list of `TextContent | ImageContent | EmbeddedResource`) from tool handlers. +- Use `format_text_response()` and `format_error_response()` helpers in server.py. +- Parameterized queries: use `SafeSqlDriver.execute_param_query(driver, sql, params)` with `{}` placeholders (not `%s` or `$1`). + +## Testing + +- **Unit tests** (`tests/unit/`): test logic without a database. Mock SQL drivers as needed. +- **Integration tests** (`tests/integration/`): run against a real PostgreSQL instance via Docker. See `tests/Dockerfile.postgres-hypopg` for the test image with pg_stat_statements and HypoPG pre-installed. +- Test runner: `uv run pytest -v --log-cli-level=INFO` +- Async tests use `pytest-asyncio`. + +## Dependencies + +- **Package manager:** `uv` (not pip/pipx for development). +- **Build system:** hatchling. +- **Key libraries:** FastMCP (`mcp` package), psycopg3 (async PostgreSQL), pglast (SQL parsing for safety), pydantic (validation), instructor (LLM integration). +- Add dev dependencies to `[dependency-groups] dev` in `pyproject.toml`. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..81cfbbb0 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,60 @@ +# CLAUDE.md β€” postgres-mcp + +Claude-specific instructions for contributing to this project. Read AGENTS.md first for shared rules (CI commands, safety guardrails, code conventions). + +**Scope:** Claude Code tool preferences, MCP-aware development workflows, and commit conventions. Does NOT duplicate content from AGENTS.md. + +## Tool Preferences + +Prefer Claude Code built-in tools over shell equivalents: + +| Prefer | Over | +|--------|------| +| `Read` | `cat`, `head`, `tail` | +| `Edit` | `sed`, `awk` | +| `Grep` | `grep`, `rg` | +| `Glob` | `find`, `ls` for file search | +| `Write` | `echo >`, heredoc | + +Use `Bash` only for commands that require shell execution (git, uv, pytest, etc.). + +## MCP-Aware Development + +When connected to a postgres-mcp instance (tools visible in MCP tool list): + +- **Use MCP tools to validate changes.** After modifying a tool handler, call the tool through MCP to verify it works. For example, after editing `list_schemas`, call `list_schemas` via MCP to confirm the output. +- **Test access mode behavior.** If your change touches SQL execution paths, verify behavior in both UNRESTRICTED and RESTRICTED modes. +- **Use `execute_sql` to inspect schema** when writing tests or debugging query-related code. + +When NOT connected to an MCP instance: + +- Run unit tests: `uv run pytest tests/unit/ -v` +- For integration tests, start the Docker test database first (see `tests/Dockerfile.postgres-hypopg`). +- Review SQL strings manually β€” the MCP tools won't be available for live validation. + +## Commit Style + +This project uses a mixed commit style. Follow these conventions: + +- **Prefix with type when applicable:** `feat:`, `fix:`, `refactor:`, `chore:`, `docs:` β€” lowercase, no scope. +- **Imperative mood for the subject line.** Example: `fix: Support PostgreSQL 12 in get_top_queries` +- **Include PR number** if merging via GitHub: `Add streamable HTTP transport support (#134)` +- **Keep subject line under 72 characters.** +- No body required for small changes. Add a body for non-obvious context. + +## Development Workflow + +1. Read the relevant source files before making changes. Start with `server.py` for tool definitions. +2. Run the full CI suite before considering work complete (see AGENTS.md for commands). +3. If adding a new MCP tool: + - Define it in `server.py` with `@mcp.tool` decorator. + - Use `get_sql_driver()` β€” this is a hard safety rule (see AGENTS.md). + - Add unit tests in `tests/unit/` and integration tests in `tests/integration/`. + +## Project-Specific Context + +- The codebase is async throughout (psycopg3 async, FastMCP). +- `execute_sql` is dynamically registered (not decorated) β€” see `main()` in server.py. +- Index tuning uses the DTA algorithm by default. LLM optimization (`method="llm"`) is experimental and requires `OPENAI_API_KEY`. +- SQL safety parsing uses `pglast` to reject COMMIT/ROLLBACK in restricted mode. +- Parameterized queries use `{}` placeholders via `SafeSqlDriver.execute_param_query()`, not `%s` or `$1`. From 161d89963b1b4d973faf2257cb1fbee0fdfcd40b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20H=C3=A4uslschmid?= Date: Fri, 20 Mar 2026 10:03:11 +0100 Subject: [PATCH 07/18] feat: add schema comments and materialized view support to metadata tools Add PostgreSQL COMMENT support to list_objects and get_object_details: - list_objects: include obj_description() for tables, views, and materialized views - get_object_details: include col_description() for column comments and obj_description() for table/view/materialized view comments Add materialized view as a new object_type: - list_objects: query pg_class with relkind='m' for materialized views - get_object_details: query pg_attribute for columns (since materialized views are not in information_schema.columns) This enables LLMs using the MCP server to discover schema documentation stored as COMMENT ON TABLE/VIEW/COLUMN statements, which is the standard PostgreSQL mechanism for schema documentation. Closes #71 --- src/postgres_mcp/server.py | 127 +++++++++++++++++++++++++++++++------ 1 file changed, 107 insertions(+), 20 deletions(-) diff --git a/src/postgres_mcp/server.py b/src/postgres_mcp/server.py index f3ba8f8b..756d93e2 100644 --- a/src/postgres_mcp/server.py +++ b/src/postgres_mcp/server.py @@ -122,7 +122,7 @@ async def list_schemas() -> ResponseType: ) async def list_objects( schema_name: str = Field(description="Schema name"), - object_type: str = Field(description="Object type: 'table', 'view', 'sequence', or 'extension'", default="table"), + object_type: str = Field(description="Object type: 'table', 'view', 'materialized_view', 'sequence', or 'extension'", default="table"), ) -> ResponseType: """List objects of a given type in a schema.""" try: @@ -133,15 +133,59 @@ async def list_objects( rows = await SafeSqlDriver.execute_param_query( sql_driver, """ - SELECT table_schema, table_name, table_type - FROM information_schema.tables - WHERE table_schema = {} AND table_type = {} - ORDER BY table_name + SELECT + t.table_schema, + t.table_name, + t.table_type, + obj_description( + (quote_ident(t.table_schema) || '.' || quote_ident(t.table_name))::regclass, + 'pg_class' + ) AS comment + FROM information_schema.tables t + WHERE t.table_schema = {} AND t.table_type = {} + ORDER BY t.table_name """, [schema_name, table_type], ) objects = ( - [{"schema": row.cells["table_schema"], "name": row.cells["table_name"], "type": row.cells["table_type"]} for row in rows] + [ + { + "schema": row.cells["table_schema"], + "name": row.cells["table_name"], + "type": row.cells["table_type"], + "comment": row.cells["comment"], + } + for row in rows + ] + if rows + else [] + ) + + elif object_type == "materialized_view": + rows = await SafeSqlDriver.execute_param_query( + sql_driver, + """ + SELECT + n.nspname AS schema_name, + c.relname AS view_name, + obj_description(c.oid, 'pg_class') AS comment + FROM pg_class c + JOIN pg_namespace n ON n.oid = c.relnamespace + WHERE c.relkind = 'm' AND n.nspname = {} + ORDER BY c.relname + """, + [schema_name], + ) + objects = ( + [ + { + "schema": row.cells["schema_name"], + "name": row.cells["view_name"], + "type": "MATERIALIZED VIEW", + "comment": row.cells["comment"], + } + for row in rows + ] if rows else [] ) @@ -197,24 +241,53 @@ async def list_objects( async def get_object_details( schema_name: str = Field(description="Schema name"), object_name: str = Field(description="Object name"), - object_type: str = Field(description="Object type: 'table', 'view', 'sequence', or 'extension'", default="table"), + object_type: str = Field(description="Object type: 'table', 'view', 'materialized_view', 'sequence', or 'extension'", default="table"), ) -> ResponseType: """Get detailed information about a database object.""" try: sql_driver = await get_sql_driver() - if object_type in ("table", "view"): - # Get columns - col_rows = await SafeSqlDriver.execute_param_query( - sql_driver, - """ - SELECT column_name, data_type, is_nullable, column_default - FROM information_schema.columns - WHERE table_schema = {} AND table_name = {} - ORDER BY ordinal_position - """, - [schema_name, object_name], - ) + if object_type in ("table", "view", "materialized_view"): + # Get columns with comments + if object_type == "materialized_view": + # Materialized views are not in information_schema.columns β€” use pg_catalog + col_rows = await SafeSqlDriver.execute_param_query( + sql_driver, + """ + SELECT + a.attname AS column_name, + pg_catalog.format_type(a.atttypid, a.atttypmod) AS data_type, + CASE WHEN a.attnotnull THEN 'NO' ELSE 'YES' END AS is_nullable, + NULL AS column_default, + col_description(c.oid, a.attnum) AS comment + FROM pg_class c + JOIN pg_namespace n ON n.oid = c.relnamespace + JOIN pg_attribute a ON a.attrelid = c.oid + WHERE n.nspname = {} AND c.relname = {} + AND c.relkind = 'm' AND a.attnum > 0 AND NOT a.attisdropped + ORDER BY a.attnum + """, + [schema_name, object_name], + ) + else: + col_rows = await SafeSqlDriver.execute_param_query( + sql_driver, + """ + SELECT + c.column_name, + c.data_type, + c.is_nullable, + c.column_default, + col_description( + (quote_ident(c.table_schema) || '.' || quote_ident(c.table_name))::regclass, + c.ordinal_position + ) AS comment + FROM information_schema.columns c + WHERE c.table_schema = {} AND c.table_name = {} + ORDER BY c.ordinal_position + """, + [schema_name, object_name], + ) columns = ( [ { @@ -222,6 +295,7 @@ async def get_object_details( "data_type": r.cells["data_type"], "is_nullable": r.cells["is_nullable"], "default": r.cells["column_default"], + "comment": r.cells["comment"], } for r in col_rows ] @@ -270,8 +344,21 @@ async def get_object_details( indexes = [{"name": r.cells["indexname"], "definition": r.cells["indexdef"]} for r in idx_rows] if idx_rows else [] + # Get table/view/materialized view comment + comment_rows = await SafeSqlDriver.execute_param_query( + sql_driver, + """ + SELECT obj_description(c.oid, 'pg_class') AS comment + FROM pg_class c + JOIN pg_namespace n ON n.oid = c.relnamespace + WHERE n.nspname = {} AND c.relname = {} + """, + [schema_name, object_name], + ) + object_comment = comment_rows[0].cells["comment"] if comment_rows else None + result = { - "basic": {"schema": schema_name, "name": object_name, "type": object_type}, + "basic": {"schema": schema_name, "name": object_name, "type": object_type, "comment": object_comment}, "columns": columns, "constraints": constraints_list, "indexes": indexes, From f9dfd62a9af1cfe481bbff1baaa982070a6aa52d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20H=C3=A4uslschmid?= Date: Fri, 20 Mar 2026 10:40:32 +0100 Subject: [PATCH 08/18] fix: add obj_description and col_description to allowed functions These pg_catalog functions are read-only and needed for the schema comment feature to work in restricted mode. --- src/postgres_mcp/sql/safe_sql.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/postgres_mcp/sql/safe_sql.py b/src/postgres_mcp/sql/safe_sql.py index 37382f0b..e2207751 100644 --- a/src/postgres_mcp/sql/safe_sql.py +++ b/src/postgres_mcp/sql/safe_sql.py @@ -417,6 +417,10 @@ class SafeSqlDriver(SqlDriver): # Validity Checking "pg_input_is_valid", "pg_input_error_info", + # Object Description/Comment Functions + "obj_description", + "col_description", + "shobj_description", # Object Definition/Information Functions "pg_get_serial_sequence", "pg_get_viewdef", From 050adc8e08b2a6f2b11d2fdb7c9d0549f4e86bd3 Mon Sep 17 00:00:00 2001 From: RajatRaiD11 <96173109+RajatRaiD11@users.noreply.github.com> Date: Fri, 20 Mar 2026 18:51:52 +0530 Subject: [PATCH 09/18] fix(security): add SQL injection prevention to ExplainPlanTool Add _validate_explain_input() using pglast to parse and validate SQL before f-string concatenation. Prevents multi-statement injection and restricts EXPLAIN ANALYZE to SELECT-only. --- src/postgres_mcp/explain/explain_plan.py | 57 ++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/src/postgres_mcp/explain/explain_plan.py b/src/postgres_mcp/explain/explain_plan.py index 3027bf06..6b17155d 100644 --- a/src/postgres_mcp/explain/explain_plan.py +++ b/src/postgres_mcp/explain/explain_plan.py @@ -7,6 +7,10 @@ from typing import TYPE_CHECKING from typing import Any +import pglast +from pglast.ast import RawStmt +from pglast.ast import SelectStmt + from ..artifacts import ErrorResult from ..artifacts import ExplainPlanArtifact from ..sql import IndexDefinition @@ -65,6 +69,11 @@ async def explain(self, sql_query: str, do_analyze: bool = False) -> ExplainPlan Returns: ExplainPlanArtifact or ErrorResult """ + # Validate input to prevent SQL injection via f-string concatenation + validation_error = self._validate_explain_input(sql_query, analyze=do_analyze) + if validation_error: + return ErrorResult(validation_error) + modified_sql_query, use_generic_plan = await self.replace_query_parameters_if_needed(sql_query) return await self._run_explain_query(modified_sql_query, analyze=do_analyze, generic_plan=use_generic_plan) @@ -94,6 +103,11 @@ async def explain_with_hypothetical_indexes( ExplainPlanArtifact or ErrorResult """ try: + # Validate input SQL to prevent injection + validation_error = self._validate_explain_input(sql_query) + if validation_error: + return ErrorResult(validation_error) + # Validate index definitions format if not isinstance(hypothetical_indexes, list): return ErrorResult(f"Expected list of index definitions, got {type(hypothetical_indexes)}") @@ -142,6 +156,44 @@ async def explain_with_hypothetical_indexes( logger.error(f"Error in explain_with_hypothetical_indexes: {e}", exc_info=True) return ErrorResult(f"Error generating explain plan with hypothetical indexes: {e}") + @staticmethod + def _validate_explain_input(sql_query: str, analyze: bool = False) -> str | None: + """Validate that the SQL input for EXPLAIN is a single, safe statement. + + Parses the input with pglast to prevent SQL injection via the f-string + concatenation used to build EXPLAIN queries. This ensures the + readOnlyHint=True annotation on explain_query is honored regardless + of the server access mode. + + Args: + sql_query: The SQL query to validate + analyze: Whether EXPLAIN ANALYZE will be used (restricts to SELECT) + + Returns: + None if valid, or an error message string if invalid + """ + if not sql_query or not sql_query.strip(): + return "SQL query cannot be empty" + + try: + parsed = pglast.parse_sql(sql_query) + except pglast.parser.ParseError as e: + return f"Invalid SQL syntax: {e}" + + # Reject multi-statement input to prevent injection via semicolons + if len(parsed) != 1: + return f"EXPLAIN input must be a single SQL statement, got {len(parsed)} statements" + + # When EXPLAIN ANALYZE is used, the query is actually executed. + # Restrict to SELECT statements to prevent data modification. + if analyze: + stmt = parsed[0] + inner = stmt.stmt if isinstance(stmt, RawStmt) else stmt + if not isinstance(inner, SelectStmt): + return "EXPLAIN ANALYZE is only supported for SELECT queries to prevent unintended data modification" + + return None + def _has_bind_variables(self, query: str) -> bool: """Check if a query contains bind variables ($1, $2, etc).""" return bool(re.search(r"\$\d+", query)) @@ -201,6 +253,11 @@ async def generate_explain_plan_with_hypothetical_indexes( The explain plan as a dictionary """ try: + # Validate input SQL to prevent injection via f-string concatenation + validation_error = self._validate_explain_input(query_text) + if validation_error: + raise ValueError(validation_error) + # Create the indexes query create_indexes_query = "SELECT hypopg_reset();" if len(indexes) > 0: From 67fd794155b4da8ae9beca6b606bf3417d5c59cf Mon Sep 17 00:00:00 2001 From: RajatRaiD11 <96173109+RajatRaiD11@users.noreply.github.com> Date: Fri, 20 Mar 2026 18:55:11 +0530 Subject: [PATCH 10/18] test: add comprehensive tests for _validate_explain_input 17 unit tests + 5 integration tests covering valid queries, multi-statement injection rejection, empty/invalid input, EXPLAIN ANALYZE DML restrictions, and COPY exfiltration prevention. --- .../explain/test_explain_input_validation.py | 173 ++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 tests/unit/explain/test_explain_input_validation.py diff --git a/tests/unit/explain/test_explain_input_validation.py b/tests/unit/explain/test_explain_input_validation.py new file mode 100644 index 00000000..5b85956b --- /dev/null +++ b/tests/unit/explain/test_explain_input_validation.py @@ -0,0 +1,173 @@ +"""Tests for SQL injection prevention in ExplainPlanTool._validate_explain_input. + +These tests verify that the EXPLAIN tool validates its input SQL to prevent +SQL injection via the f-string concatenation used to build EXPLAIN queries. +This ensures the readOnlyHint=True annotation on explain_query is honored +regardless of the server access mode. +""" + +from unittest.mock import AsyncMock +from unittest.mock import MagicMock + +import pytest +import pytest_asyncio + +from postgres_mcp.artifacts import ErrorResult +from postgres_mcp.explain import ExplainPlanTool + + +@pytest_asyncio.fixture +async def mock_sql_driver(): + """Create a mock SQL driver for testing.""" + driver = MagicMock() + driver.execute_query = AsyncMock() + return driver + + +# ============================================================================ +# _validate_explain_input unit tests +# ============================================================================ + + +class TestValidateExplainInput: + """Tests for the _validate_explain_input static method.""" + + def test_valid_select(self): + """Valid SELECT queries should pass validation.""" + assert ExplainPlanTool._validate_explain_input("SELECT * FROM users") is None + assert ExplainPlanTool._validate_explain_input("SELECT id, name FROM users WHERE age > 18") is None + assert ExplainPlanTool._validate_explain_input("SELECT u.name, o.total FROM users u JOIN orders o ON u.id = o.user_id") is None + + def test_valid_select_with_cte(self): + """SELECT with CTE (WITH clause) should pass.""" + assert ExplainPlanTool._validate_explain_input("WITH active AS (SELECT * FROM users WHERE active) SELECT * FROM active") is None + + def test_valid_select_with_subquery(self): + """SELECT with subquery should pass.""" + assert ExplainPlanTool._validate_explain_input("SELECT * FROM users WHERE id IN (SELECT user_id FROM orders)") is None + + def test_valid_insert_without_analyze(self): + """INSERT should pass validation when analyze=False (EXPLAIN without ANALYZE is safe).""" + assert ExplainPlanTool._validate_explain_input("INSERT INTO users (name) VALUES ('test')", analyze=False) is None + + def test_valid_update_without_analyze(self): + """UPDATE should pass validation when analyze=False.""" + assert ExplainPlanTool._validate_explain_input("UPDATE users SET name = 'test' WHERE id = 1", analyze=False) is None + + def test_valid_delete_without_analyze(self): + """DELETE should pass validation when analyze=False.""" + assert ExplainPlanTool._validate_explain_input("DELETE FROM users WHERE id = 1", analyze=False) is None + + def test_reject_multi_statement_injection(self): + """Multi-statement input should be rejected (prevents SQL injection).""" + result = ExplainPlanTool._validate_explain_input("SELECT 1; DROP TABLE users") + assert result is not None + assert "single SQL statement" in result + + def test_reject_multi_statement_with_comment(self): + """Multi-statement with comment-based injection should be rejected.""" + result = ExplainPlanTool._validate_explain_input("SELECT 1; DROP TABLE users; --") + assert result is not None + assert "single SQL statement" in result + + def test_reject_empty_query(self): + """Empty queries should be rejected.""" + assert ExplainPlanTool._validate_explain_input("") is not None + assert ExplainPlanTool._validate_explain_input(" ") is not None + + def test_reject_invalid_sql(self): + """Invalid SQL syntax should be rejected.""" + result = ExplainPlanTool._validate_explain_input("NOT VALID SQL AT ALL") + assert result is not None + assert "Invalid SQL syntax" in result + + def test_analyze_rejects_delete(self): + """EXPLAIN ANALYZE should reject DELETE to prevent data modification.""" + result = ExplainPlanTool._validate_explain_input("DELETE FROM users WHERE 1=1", analyze=True) + assert result is not None + assert "SELECT" in result + + def test_analyze_rejects_update(self): + """EXPLAIN ANALYZE should reject UPDATE to prevent data modification.""" + result = ExplainPlanTool._validate_explain_input("UPDATE users SET name = 'hacked'", analyze=True) + assert result is not None + assert "SELECT" in result + + def test_analyze_rejects_insert(self): + """EXPLAIN ANALYZE should reject INSERT to prevent data modification.""" + result = ExplainPlanTool._validate_explain_input("INSERT INTO users (name) VALUES ('test')", analyze=True) + assert result is not None + assert "SELECT" in result + + def test_analyze_allows_select(self): + """EXPLAIN ANALYZE should allow SELECT queries.""" + assert ExplainPlanTool._validate_explain_input("SELECT * FROM users", analyze=True) is None + + def test_reject_drop_table(self): + """DDL statements should be rejected as multi-statement or invalid.""" + # DROP TABLE alone is a single statement but not a valid EXPLAIN target in pglast + result = ExplainPlanTool._validate_explain_input("SELECT 1; DROP TABLE users") + assert result is not None + + def test_reject_copy_injection(self): + """COPY-based data exfiltration should be rejected.""" + result = ExplainPlanTool._validate_explain_input("SELECT 1; COPY (SELECT * FROM pg_shadow) TO '/tmp/pwned.csv'") + assert result is not None + + def test_valid_select_with_bind_params(self): + """Queries with bind parameters ($1, $2) should pass validation.""" + assert ExplainPlanTool._validate_explain_input("SELECT * FROM users WHERE id = $1") is None + assert ExplainPlanTool._validate_explain_input("SELECT * FROM users WHERE id = $1 AND name = $2") is None + + +# ============================================================================ +# Integration tests: explain method blocks injection +# ============================================================================ + + +class TestExplainBlocksInjection: + """Tests that the explain() method blocks SQL injection attempts.""" + + @pytest.mark.asyncio + async def test_explain_blocks_multi_statement(self, mock_sql_driver): + """explain() should return ErrorResult for multi-statement injection.""" + tool = ExplainPlanTool(sql_driver=mock_sql_driver) + result = await tool.explain("SELECT 1; DROP TABLE users") + assert isinstance(result, ErrorResult) + # Verify the query was never sent to the database + mock_sql_driver.execute_query.assert_not_awaited() + + @pytest.mark.asyncio + async def test_explain_analyze_blocks_delete(self, mock_sql_driver): + """explain_analyze() should block DELETE statements.""" + tool = ExplainPlanTool(sql_driver=mock_sql_driver) + result = await tool.explain_analyze("DELETE FROM users WHERE 1=1") + assert isinstance(result, ErrorResult) + mock_sql_driver.execute_query.assert_not_awaited() + + @pytest.mark.asyncio + async def test_explain_analyze_blocks_update(self, mock_sql_driver): + """explain_analyze() should block UPDATE statements.""" + tool = ExplainPlanTool(sql_driver=mock_sql_driver) + result = await tool.explain_analyze("UPDATE users SET admin = true") + assert isinstance(result, ErrorResult) + mock_sql_driver.execute_query.assert_not_awaited() + + @pytest.mark.asyncio + async def test_explain_blocks_empty_input(self, mock_sql_driver): + """explain() should return ErrorResult for empty input.""" + tool = ExplainPlanTool(sql_driver=mock_sql_driver) + result = await tool.explain("") + assert isinstance(result, ErrorResult) + mock_sql_driver.execute_query.assert_not_awaited() + + @pytest.mark.asyncio + async def test_explain_with_hypothetical_indexes_blocks_injection(self, mock_sql_driver): + """explain_with_hypothetical_indexes() should block SQL injection.""" + tool = ExplainPlanTool(sql_driver=mock_sql_driver) + result = await tool.explain_with_hypothetical_indexes( + "SELECT 1; DROP TABLE users", + [{"table": "users", "columns": ["email"]}], + ) + assert isinstance(result, ErrorResult) + mock_sql_driver.execute_query.assert_not_awaited() From d7297c15084a0649452a1720e565288286b60745 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20H=C3=A4uslschmid?= Date: Fri, 20 Mar 2026 21:42:24 +0100 Subject: [PATCH 11/18] feat: improve tool descriptions for LLM discoverability Add "PostgreSQL" keyword to all tool descriptions so LLM search can find them. Make get_object_details and list_objects descriptions explicit about returned data (columns, comments, constraints, indexes). --- src/postgres_mcp/server.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/postgres_mcp/server.py b/src/postgres_mcp/server.py index 756d93e2..5fdcffcb 100644 --- a/src/postgres_mcp/server.py +++ b/src/postgres_mcp/server.py @@ -82,7 +82,7 @@ def format_error_response(error: str) -> ResponseType: @mcp.tool( - description="List all schemas in the database", + description="List all schemas in the PostgreSQL database with their owners and types.", annotations=ToolAnnotations( title="List Schemas", readOnlyHint=True, @@ -114,7 +114,7 @@ async def list_schemas() -> ResponseType: @mcp.tool( - description="List objects in a schema", + description="List tables, views, materialized views, sequences, or extensions in a PostgreSQL schema. Returns names and comments/descriptions for each object.", annotations=ToolAnnotations( title="List Objects", readOnlyHint=True, @@ -232,7 +232,7 @@ async def list_objects( @mcp.tool( - description="Show detailed information about a database object", + description="Show columns (with types, nullability, defaults, and comments), constraints, indexes, and the table/view comment for a PostgreSQL object. Use this BEFORE writing queries to understand the schema.", annotations=ToolAnnotations( title="Get Object Details", readOnlyHint=True, @@ -414,7 +414,7 @@ async def get_object_details( @mcp.tool( - description="Explains the execution plan for a SQL query, showing how the database will execute it and provides detailed cost estimates.", + description="Explains the PostgreSQL execution plan for a SQL query, showing how the database will execute it and provides detailed cost estimates.", annotations=ToolAnnotations( title="Explain Query", readOnlyHint=True, @@ -515,7 +515,7 @@ async def execute_sql( @mcp.tool( - description="Analyze frequently executed queries in the database and recommend optimal indexes", + description="Analyze frequently executed PostgreSQL queries and recommend optimal indexes.", annotations=ToolAnnotations( title="Analyze Workload Indexes", readOnlyHint=True, @@ -542,7 +542,7 @@ async def analyze_workload_indexes( @mcp.tool( - description="Analyze a list of (up to 10) SQL queries and recommend optimal indexes", + description="Analyze a list of (up to 10) PostgreSQL queries and recommend optimal indexes.", annotations=ToolAnnotations( title="Analyze Query Indexes", readOnlyHint=True, @@ -575,7 +575,7 @@ async def analyze_query_indexes( @mcp.tool( - description="Analyzes database health. Here are the available health checks:\n" + description="Analyzes PostgreSQL database health. Here are the available health checks:\n" "- index - checks for invalid, duplicate, and bloated indexes\n" "- connection - checks the number of connection and their utilization\n" "- vacuum - checks vacuum health for transaction id wraparound\n" @@ -703,7 +703,7 @@ async def main(): else: mcp.add_tool( execute_sql, - description="Execute a read-only SQL query", + description="Execute a read-only SQL query against the PostgreSQL database and return the results.", annotations=ToolAnnotations( title="Execute SQL (Read-Only)", readOnlyHint=True, From fd0f09989f49d69fbfc30981022487edf6b807d4 Mon Sep 17 00:00:00 2001 From: Brian-kipkoech-mutai Date: Mon, 11 May 2026 17:09:13 +0300 Subject: [PATCH 12/18] refactor: Modernize type hints to use Python 3.12 built-in generics Replace legacy typing imports (List, Dict, Optional, Union) with built-in generics (list, dict, X | None, X | Y) across 7 source files. Removes the UP006/UP035/UP045 ruff ignore entries and the TODO comment referencing issue #129. --- pyproject.toml | 4 ---- .../database_health/database_health.py | 3 +-- .../database_health/replication_calc.py | 7 +++---- src/postgres_mcp/index/presentation.py | 6 ++---- src/postgres_mcp/server.py | 6 ++---- src/postgres_mcp/sql/safe_sql.py | 3 +-- src/postgres_mcp/sql/sql_driver.py | 15 ++++++--------- src/postgres_mcp/top_queries/top_queries_calc.py | 3 +-- 8 files changed, 16 insertions(+), 31 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3160a03a..6bb526e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,11 +65,7 @@ lint.select = [ "RUF" # ruff-specific rules ] -# TODO: Remove these ignores when fixing #129 (code modernization) lint.ignore = [ - "UP006", # Use `list` instead of `List` for type annotations - "UP035", # Import from `collections.abc` instead of `typing` - "UP045", # Use `X | None` instead of `Optional[X]` "RUF059", # Unused unpacked variable "RUF100", # Unused noqa directive ] diff --git a/src/postgres_mcp/database_health/database_health.py b/src/postgres_mcp/database_health/database_health.py index ecf94924..4b1e6bf0 100644 --- a/src/postgres_mcp/database_health/database_health.py +++ b/src/postgres_mcp/database_health/database_health.py @@ -2,7 +2,6 @@ import logging from enum import Enum -from typing import List import mcp.types as types @@ -14,7 +13,7 @@ from .sequence_health_calc import SequenceHealthCalc from .vacuum_health_calc import VacuumHealthCalc -ResponseType = List[types.TextContent | types.ImageContent | types.EmbeddedResource] +ResponseType = list[types.TextContent | types.ImageContent | types.EmbeddedResource] logger = logging.getLogger(__name__) diff --git a/src/postgres_mcp/database_health/replication_calc.py b/src/postgres_mcp/database_health/replication_calc.py index 42b7882c..93c2fa20 100644 --- a/src/postgres_mcp/database_health/replication_calc.py +++ b/src/postgres_mcp/database_health/replication_calc.py @@ -1,5 +1,4 @@ from dataclasses import dataclass -from typing import Optional from ..sql import SqlDriver @@ -14,7 +13,7 @@ class ReplicationSlot: @dataclass class ReplicationMetrics: is_replica: bool - replication_lag_seconds: Optional[float] + replication_lag_seconds: float | None is_replicating: bool replication_slots: list[ReplicationSlot] @@ -22,7 +21,7 @@ class ReplicationMetrics: class ReplicationCalc: def __init__(self, sql_driver: SqlDriver): self.sql_driver = sql_driver - self._server_version: Optional[int] = None + self._server_version: int | None = None self._feature_support: dict[str, bool] = {} async def replication_health_check(self) -> str: @@ -85,7 +84,7 @@ async def _is_replica(self) -> bool: result_list = [dict(x.cells) for x in result] if result is not None else [] return bool(result_list[0]["pg_is_in_recovery"]) if result_list else False - async def _get_replication_lag(self) -> Optional[float]: + async def _get_replication_lag(self) -> float | None: """Get replication lag in seconds.""" if not self._feature_supported("replication_lag"): return None diff --git a/src/postgres_mcp/index/presentation.py b/src/postgres_mcp/index/presentation.py index 5e806c2a..32abe76e 100644 --- a/src/postgres_mcp/index/presentation.py +++ b/src/postgres_mcp/index/presentation.py @@ -3,8 +3,6 @@ import logging import os from typing import Any -from typing import Dict -from typing import List import humanize @@ -169,7 +167,7 @@ async def _execute_analysis( logger.error(f"Error analyzing queries: {e}", exc_info=True) return {"error": f"Error analyzing queries: {e}"} - def _build_recommendations_list(self, session: IndexTuningResult) -> List[Dict[str, Any]]: + def _build_recommendations_list(self, session: IndexTuningResult) -> list[dict[str, Any]]: recommendations = [] for index_apply_order, rec in enumerate(session.recommendations): rec_dict = { @@ -200,7 +198,7 @@ def _build_recommendations_list(self, session: IndexTuningResult) -> List[Dict[s recommendations.append(rec_dict) return recommendations - async def _generate_query_impact(self, session: IndexTuningResult) -> List[Dict[str, Any]]: + async def _generate_query_impact(self, session: IndexTuningResult) -> list[dict[str, Any]]: """ Generate the query impact section showing before/after explain plans. diff --git a/src/postgres_mcp/server.py b/src/postgres_mcp/server.py index f3ba8f8b..2f567dec 100644 --- a/src/postgres_mcp/server.py +++ b/src/postgres_mcp/server.py @@ -7,9 +7,7 @@ import sys from enum import Enum from typing import Any -from typing import List from typing import Literal -from typing import Union import mcp.types as types from mcp.server.fastmcp import FastMCP @@ -41,7 +39,7 @@ PG_STAT_STATEMENTS = "pg_stat_statements" HYPOPG_EXTENSION = "hypopg" -ResponseType = List[types.TextContent | types.ImageContent | types.EmbeddedResource] +ResponseType = list[types.TextContent | types.ImageContent | types.EmbeddedResource] logger = logging.getLogger(__name__) @@ -59,7 +57,7 @@ class AccessMode(str, Enum): shutdown_in_progress = False -async def get_sql_driver() -> Union[SqlDriver, SafeSqlDriver]: +async def get_sql_driver() -> SqlDriver | SafeSqlDriver: """Get the appropriate SQL driver based on the current access mode.""" base_driver = SqlDriver(conn=db_connection) diff --git a/src/postgres_mcp/sql/safe_sql.py b/src/postgres_mcp/sql/safe_sql.py index 37382f0b..92acc3ff 100644 --- a/src/postgres_mcp/sql/safe_sql.py +++ b/src/postgres_mcp/sql/safe_sql.py @@ -5,7 +5,6 @@ import re from typing import Any from typing import ClassVar -from typing import Optional import pglast from pglast.ast import A_ArrayExpr @@ -982,7 +981,7 @@ async def execute_query( query: LiteralString, params: list[Any] | None = None, force_readonly: bool = True, # do not use value passed in - ) -> Optional[list[SqlDriver.RowResult]]: # noqa: UP007 + ) -> list[SqlDriver.RowResult] | None: """Execute a query after validating it is safe""" self._validate(query) diff --git a/src/postgres_mcp/sql/sql_driver.py b/src/postgres_mcp/sql/sql_driver.py index 5beacb03..1fa08631 100644 --- a/src/postgres_mcp/sql/sql_driver.py +++ b/src/postgres_mcp/sql/sql_driver.py @@ -4,9 +4,6 @@ import re from dataclasses import dataclass from typing import Any -from typing import Dict -from typing import List -from typing import Optional from urllib.parse import urlparse from urllib.parse import urlunparse @@ -62,13 +59,13 @@ def obfuscate_password(text: str | None) -> str | None: class DbConnPool: """Database connection manager using psycopg's connection pool.""" - def __init__(self, connection_url: Optional[str] = None): + def __init__(self, connection_url: str | None = None): self.connection_url = connection_url self.pool: AsyncConnectionPool | None = None self._is_valid = False self._last_error = None - async def pool_connect(self, connection_url: Optional[str] = None) -> AsyncConnectionPool: + async def pool_connect(self, connection_url: str | None = None) -> AsyncConnectionPool: """Initialize connection pool with retry logic.""" # If we already have a valid pool, return it if self.pool and self._is_valid: @@ -131,7 +128,7 @@ def is_valid(self) -> bool: return self._is_valid @property - def last_error(self) -> Optional[str]: + def last_error(self) -> str | None: """Get the last error message.""" return self._last_error @@ -143,7 +140,7 @@ class SqlDriver: class RowResult: """Simple class to match the Griptape RowResult interface.""" - cells: Dict[str, Any] + cells: dict[str, Any] def __init__( self, @@ -184,7 +181,7 @@ async def execute_query( query: LiteralString, params: list[Any] | None = None, force_readonly: bool = False, - ) -> Optional[List[RowResult]]: + ) -> list[RowResult] | None: """ Execute a query and return results. @@ -221,7 +218,7 @@ async def execute_query( raise e - async def _execute_with_connection(self, connection, query, params, force_readonly) -> Optional[List[RowResult]]: + async def _execute_with_connection(self, connection, query, params, force_readonly) -> list[RowResult] | None: """Execute query with the given connection.""" transaction_started = False try: diff --git a/src/postgres_mcp/top_queries/top_queries_calc.py b/src/postgres_mcp/top_queries/top_queries_calc.py index f3fcba1c..58198cff 100644 --- a/src/postgres_mcp/top_queries/top_queries_calc.py +++ b/src/postgres_mcp/top_queries/top_queries_calc.py @@ -2,7 +2,6 @@ from dataclasses import dataclass from typing import Literal from typing import LiteralString -from typing import Union from typing import cast from ..sql import SafeSqlDriver @@ -82,7 +81,7 @@ def _get_pg_stat_statements_columns(pg_version: int) -> PgStatStatementsColumns: class TopQueriesCalc: """Tool for retrieving the slowest SQL queries.""" - def __init__(self, sql_driver: Union[SqlDriver, SafeSqlDriver]): + def __init__(self, sql_driver: SqlDriver | SafeSqlDriver): self.sql_driver = sql_driver async def get_top_queries_by_time(self, limit: int = 10, sort_by: Literal["total", "mean"] = "mean") -> str: From 74a8f1871c0258b9b1d185e2da729d408a42d3f7 Mon Sep 17 00:00:00 2001 From: Pratik Khadloya Date: Wed, 17 Jun 2026 11:19:13 +0530 Subject: [PATCH 13/18] Connect to database lazily with configurable idle timeout Establish the connection pool on first tool use instead of at server startup, and let idle connections be reaped, so defining many database MCP configs no longer means every server holds an open Postgres connection from session start. - server.py: store the database URL without connecting eagerly; the lazy path in SqlDriver.execute_query opens the pool on first use. - sql_driver.py: pool now uses min_size=0 with a max_idle reaper so unused/idle connections release. - Add --max-idle flag and DATABASE_MAX_IDLE env var (default 300s) to tune the idle timeout; invalid values fall back to the default via a validating max_idle property. - README: document lazy connections and the idle timeout with examples. Co-Authored-By: Claude Opus 4.8 (1M context) --- README.md | 36 +++++++++++++++++++++++++ src/postgres_mcp/server.py | 43 +++++++++++++++++++++--------- src/postgres_mcp/sql/sql_driver.py | 32 +++++++++++++++++++--- 3 files changed, 96 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index d5005ed6..95069f03 100644 --- a/README.md +++ b/README.md @@ -227,6 +227,42 @@ Postgres MCP Pro supports multiple *access modes* to give you control over the o To use restricted mode, replace `--access-mode=unrestricted` with `--access-mode=restricted` in the configuration examples above. +##### Idle Connection Timeout + +Postgres MCP Pro connects to the database **lazily** β€” no connection is opened when the server starts, only on the first tool call that needs the database. Once a connection has been idle for a while it is reaped, so a server you configure but don't use holds zero Postgres connections. This is helpful when you define many database configs and don't want every server holding an open connection from session start. + +The idle timeout defaults to **300 seconds** and is configurable with the `--max-idle` flag (in seconds), or the `DATABASE_MAX_IDLE` environment variable. The flag takes precedence over the environment variable, and invalid values (non-numeric, zero, or negative) fall back to the default. + +```json +{ + "mcpServers": { + "postgres": { + "command": "uvx", + "args": [ + "postgres-mcp", + "--access-mode=unrestricted", + "--max-idle=120" + ], + "env": { + "DATABASE_URI": "postgresql://username:password@localhost:5432/dbname" + } + } + } +} +``` + +Equivalently, using the environment variable instead of the flag: + +```json + "env": { + "DATABASE_URI": "postgresql://username:password@localhost:5432/dbname", + "DATABASE_MAX_IDLE": "120" + } +``` + +A reaped connection is re-established transparently on the next tool call, restarting the idle timer. Lower values release connections faster (good for many-database setups); higher values keep connections warm to avoid reconnect latency on frequently-used databases. + + #### Other MCP Clients Many MCP clients have similar configuration files to Claude Desktop, and you can adapt the examples above to work with the client of your choice. diff --git a/src/postgres_mcp/server.py b/src/postgres_mcp/server.py index f3ba8f8b..769a0b45 100644 --- a/src/postgres_mcp/server.py +++ b/src/postgres_mcp/server.py @@ -31,7 +31,6 @@ from .sql import SafeSqlDriver from .sql import SqlDriver from .sql import check_hypopg_installation_status -from .sql import obfuscate_password from .top_queries import TopQueriesCalc # Initialize FastMCP with default settings @@ -596,9 +595,33 @@ async def main(): default=8000, help="Port for streamable HTTP server (default: 8000)", ) + parser.add_argument( + "--max-idle", + type=int, + default=None, + help="Seconds an unused database connection stays open before it is reaped " + f"(default: {DbConnPool.DEFAULT_MAX_IDLE}). Can also be set via the DATABASE_MAX_IDLE " + "environment variable. Useful when defining many database configs so idle servers " + "release their connections.", + ) args = parser.parse_args() + # Configure the idle-connection timeout (CLI flag takes precedence over env var). + # Invalid values are rejected by DbConnPool.max_idle, which falls back to the default. + max_idle_env = os.environ.get("DATABASE_MAX_IDLE") + if args.max_idle is not None: + db_connection.max_idle = args.max_idle + elif max_idle_env is not None: + try: + db_connection.max_idle = int(max_idle_env) + except ValueError: + logger.warning( + "Ignoring non-numeric DATABASE_MAX_IDLE=%r; using default of %s seconds", + max_idle_env, + DbConnPool.DEFAULT_MAX_IDLE, + ) + # Store the access mode in the global variable global current_access_mode current_access_mode = AccessMode(args.access_mode) @@ -633,17 +656,13 @@ async def main(): "Error: No database URL provided. Please specify via 'DATABASE_URI' environment variable or command-line argument.", ) - # Initialize database connection pool - try: - await db_connection.pool_connect(database_url) - logger.info("Successfully connected to database and initialized connection pool") - except Exception as e: - logger.warning( - f"Could not connect to database: {obfuscate_password(str(e))}", - ) - logger.warning( - "The MCP server will start but database operations will fail until a valid connection is established.", - ) + # Store the database URL but do NOT connect yet. The connection pool is + # established lazily on the first tool call that needs it (see + # SqlDriver.execute_query -> DbConnPool.pool_connect). This lets you define + # many database MCP configs without each server holding an open Postgres + # connection from session start. + db_connection.connection_url = database_url + logger.info("Database URL configured; connection will be established on first use (lazy)") # Set up proper shutdown handling try: diff --git a/src/postgres_mcp/sql/sql_driver.py b/src/postgres_mcp/sql/sql_driver.py index 5beacb03..2cd5f89e 100644 --- a/src/postgres_mcp/sql/sql_driver.py +++ b/src/postgres_mcp/sql/sql_driver.py @@ -62,12 +62,32 @@ def obfuscate_password(text: str | None) -> str | None: class DbConnPool: """Database connection manager using psycopg's connection pool.""" - def __init__(self, connection_url: Optional[str] = None): + # Default seconds an unused connection stays open before being reaped. + DEFAULT_MAX_IDLE = 300 + + def __init__(self, connection_url: Optional[str] = None, max_idle: int = DEFAULT_MAX_IDLE): self.connection_url = connection_url + self.max_idle = max_idle # validated via the property setter self.pool: AsyncConnectionPool | None = None self._is_valid = False self._last_error = None + @property + def max_idle(self) -> int: + """Seconds an unused connection stays open before being reaped.""" + return self._max_idle + + @max_idle.setter + def max_idle(self, value: Optional[int]) -> None: + # psycopg_pool does not range-check max_idle, so a non-positive value would + # schedule a degenerate (zero/negative-interval) pool-shrink task. Reject it + # and fall back to the default instead. + if value is None or value <= 0: + logger.warning("Ignoring invalid max_idle=%r; using default of %s seconds", value, self.DEFAULT_MAX_IDLE) + self._max_idle = self.DEFAULT_MAX_IDLE + else: + self._max_idle = value + async def pool_connect(self, connection_url: Optional[str] = None) -> AsyncConnectionPool: """Initialize connection pool with retry logic.""" # If we already have a valid pool, return it @@ -85,11 +105,17 @@ async def pool_connect(self, connection_url: Optional[str] = None) -> AsyncConne await self.close() try: - # Configure connection pool with appropriate settings + # Configure connection pool with appropriate settings. + # min_size=0 so an idle pool holds NO open connections; combined with + # lazy pool_connect (called on first tool use) this means a server that + # is configured but never used keeps zero Postgres connections. + # max_idle reaps connections after a period of inactivity, so a database + # that is touched once and then left alone releases its connection. self.pool = AsyncConnectionPool( conninfo=url, - min_size=1, + min_size=0, max_size=5, + max_idle=self.max_idle, # close idle connections after this many seconds open=False, # Don't connect immediately, let's do it explicitly ) From 05c2d0f40044af3d0e45fcb359ec8e580bd265b5 Mon Sep 17 00:00:00 2001 From: Evan Culver Date: Sat, 1 Aug 2026 14:38:37 -0700 Subject: [PATCH 14/18] fix: stop pool churn and pool leaks on query errors (#98) Two defects in DbConnPool/SqlDriver combined to produce the behavior reported in #98 (ever-incrementing pool-N names, 'the pool is closed' errors, and memory growth proportional to usage): 1. SqlDriver.execute_query marked the shared pool invalid on *every* exception, including query-level errors like bad SQL or a statement timeout. The next call then tore down and rebuilt the pool, so each malformed query churned a pool. Now only connection-level errors (OperationalError/InterfaceError, excluding QueryCanceled) invalidate the pool. 2. DbConnPool.pool_connect had no synchronization: concurrent callers that both observed an invalid pool each built an AsyncConnectionPool, and every overwritten pool was left open forever - leaking its connections and worker tasks - while callers holding a pool that a later winner closed failed with 'the pool ... is closed'. Pool (re)creation is now serialized with an asyncio.Lock and re-checked under the lock. Also pass check=AsyncConnectionPool.check_connection so connections broken while idle (server restart, idle timeout) are discarded on checkout instead of surfacing as query errors that would previously have poisoned the pool. In production we observed this as steady ~6MiB/day memory growth in a long-lived streamable-http deployment, resetting only on pod restart. Co-Authored-By: Claude Fable 5 --- src/postgres_mcp/sql/sql_driver.py | 113 +++++++++++++++++++--------- tests/unit/sql/test_db_conn_pool.py | 59 +++++++++++++++ tests/unit/sql/test_sql_driver.py | 67 +++++++++++++++++ 3 files changed, 202 insertions(+), 37 deletions(-) diff --git a/src/postgres_mcp/sql/sql_driver.py b/src/postgres_mcp/sql/sql_driver.py index 5beacb03..099ac5c7 100644 --- a/src/postgres_mcp/sql/sql_driver.py +++ b/src/postgres_mcp/sql/sql_driver.py @@ -1,5 +1,6 @@ """SQL driver adapter for PostgreSQL connections.""" +import asyncio import logging import re from dataclasses import dataclass @@ -10,6 +11,7 @@ from urllib.parse import urlparse from urllib.parse import urlunparse +import psycopg from psycopg.rows import dict_row from psycopg_pool import AsyncConnectionPool from typing_extensions import LiteralString @@ -17,6 +19,21 @@ logger = logging.getLogger(__name__) +def _invalidates_pool(e: BaseException) -> bool: + """Whether an error means the connection pool itself is unusable. + + Query-level failures (bad SQL, constraint violations, ...) and statement + timeouts leave the underlying connections healthy, so the pool must + survive them. Only connection-level errors (server gone away, broken + socket, pool timeout/closed β€” all OperationalError or InterfaceError) + warrant rebuilding the pool. + """ + if isinstance(e, psycopg.errors.QueryCanceled): + # Statement timeout / cancellation: the connection is fine. + return False + return isinstance(e, (psycopg.OperationalError, psycopg.InterfaceError)) + + def obfuscate_password(text: str | None) -> str | None: """ Obfuscate password in any text containing connection information. @@ -67,6 +84,7 @@ def __init__(self, connection_url: Optional[str] = None): self.pool: AsyncConnectionPool | None = None self._is_valid = False self._last_error = None + self._connect_lock = asyncio.Lock() async def pool_connect(self, connection_url: Optional[str] = None) -> AsyncConnectionPool: """Initialize connection pool with retry logic.""" @@ -74,44 +92,60 @@ async def pool_connect(self, connection_url: Optional[str] = None) -> AsyncConne if self.pool and self._is_valid: return self.pool - url = connection_url or self.connection_url - self.connection_url = url - if not url: - self._is_valid = False - self._last_error = "Database connection URL not provided" - raise ValueError(self._last_error) + # Serialize pool (re)creation. Without the lock, concurrent callers + # that each observed an invalid pool would each build an + # AsyncConnectionPool and assign it to self.pool; every overwritten + # pool is left open forever β€” leaking its connections and worker + # tasks β€” and callers still holding a pool that a later winner + # closed fail with "the pool 'pool-N' is closed". + async with self._connect_lock: + # Re-check under the lock: another task may have already rebuilt + # the pool while we were waiting. + if self.pool and self._is_valid: + return self.pool + + url = connection_url or self.connection_url + self.connection_url = url + if not url: + self._is_valid = False + self._last_error = "Database connection URL not provided" + raise ValueError(self._last_error) - # Close any existing pool before creating a new one - await self.close() + # Close any existing pool before creating a new one + await self.close() - try: - # Configure connection pool with appropriate settings - self.pool = AsyncConnectionPool( - conninfo=url, - min_size=1, - max_size=5, - open=False, # Don't connect immediately, let's do it explicitly - ) - - # Open the pool explicitly - await self.pool.open() - - # Test the connection pool by executing a simple query - async with self.pool.connection() as conn: - async with conn.cursor() as cursor: - await cursor.execute("SELECT 1") - - self._is_valid = True - self._last_error = None - return self.pool - except Exception as e: - self._is_valid = False - self._last_error = str(e) + try: + # Configure connection pool with appropriate settings + self.pool = AsyncConnectionPool( + conninfo=url, + min_size=1, + max_size=5, + open=False, # Don't connect immediately, let's do it explicitly + # Validate connections on checkout so ones broken while + # idle (server restart, idle timeout) are discarded and + # replaced instead of surfacing as query errors. + check=AsyncConnectionPool.check_connection, + ) + + # Open the pool explicitly + await self.pool.open() + + # Test the connection pool by executing a simple query + async with self.pool.connection() as conn: + async with conn.cursor() as cursor: + await cursor.execute("SELECT 1") + + self._is_valid = True + self._last_error = None + return self.pool + except Exception as e: + self._is_valid = False + self._last_error = str(e) - # Clean up failed pool - await self.close() + # Clean up failed pool + await self.close() - raise ValueError(f"Connection attempt failed: {obfuscate_password(str(e))}") from e + raise ValueError(f"Connection attempt failed: {obfuscate_password(str(e))}") from e async def close(self) -> None: """Close the connection pool.""" @@ -212,10 +246,15 @@ async def execute_query( # Direct connection approach return await self._execute_with_connection(self.conn, query, params, force_readonly=force_readonly) except Exception as e: - # Mark pool as invalid if there was a connection issue + # Mark pool as invalid only on connection-level errors. Doing it + # for every exception meant any bad SQL statement poisoned the + # shared pool, and the next call tore it down and built a new + # one β€” the pool churn and "the pool 'pool-N' is closed" errors + # of issue #98. if self.conn and self.is_pool: - self.conn._is_valid = False # type: ignore - self.conn._last_error = str(e) # type: ignore + if _invalidates_pool(e): + self.conn._is_valid = False # type: ignore + self.conn._last_error = str(e) # type: ignore elif self.conn and not self.is_pool: self.conn = None diff --git a/tests/unit/sql/test_db_conn_pool.py b/tests/unit/sql/test_db_conn_pool.py index e916d03a..d77398a0 100644 --- a/tests/unit/sql/test_db_conn_pool.py +++ b/tests/unit/sql/test_db_conn_pool.py @@ -212,3 +212,62 @@ async def test_connection_url_property(): # Change the URL db_pool.connection_url = "postgresql://newuser:newpass@otherhost/otherdb" assert db_pool.connection_url == "postgresql://newuser:newpass@otherhost/otherdb" + + +def _wired_pool_mock(): + """A pool mock supporting `async with pool.connection()` -> `async with conn.cursor()`. + + Built with mock's native async-context-manager support: `.cursor` and + `.connection` are sync callables returning MagicMocks, whose `__aenter__` + (an AsyncMock on MagicMock) yields the next object. + """ + import asyncio + + cursor_cm = MagicMock() + cursor_cm.__aenter__.return_value = AsyncMock() + + connection = MagicMock() + connection.cursor = MagicMock(return_value=cursor_cm) + + conn_cm = MagicMock() + conn_cm.__aenter__.return_value = connection + + pool = MagicMock() + pool.connection = MagicMock(return_value=conn_cm) + + # pool.open() must actually yield to the event loop so concurrent + # pool_connect calls interleave the way they do against a real database. + async def slow_open(): + await asyncio.sleep(0) + + pool.open = AsyncMock(side_effect=slow_open) + pool.close = AsyncMock() + return pool + + +@pytest.mark.asyncio +async def test_concurrent_pool_connect_creates_single_pool(): + """Concurrent callers reconnecting must share one pool (issue #98). + + Without serialization, every caller that observes an invalid pool builds + its own AsyncConnectionPool; all but the last-assigned pool are leaked + (never closed, still holding connections and worker tasks), and callers + holding a pool a later winner closed fail with "the pool ... is closed". + """ + import asyncio + + created_pools = [] + + def make_pool(*args, **kwargs): + pool = _wired_pool_mock() + created_pools.append(pool) + return pool + + with patch("postgres_mcp.sql.sql_driver.AsyncConnectionPool", side_effect=make_pool): + db_pool = DbConnPool("postgresql://user:pass@localhost/db") + pools = await asyncio.gather(*(db_pool.pool_connect() for _ in range(10))) + + assert len(created_pools) == 1 + assert all(pool is created_pools[0] for pool in pools) + assert db_pool.is_valid + created_pools[0].close.assert_not_called() diff --git a/tests/unit/sql/test_sql_driver.py b/tests/unit/sql/test_sql_driver.py index 4033537d..a5c92a6b 100644 --- a/tests/unit/sql/test_sql_driver.py +++ b/tests/unit/sql/test_sql_driver.py @@ -365,3 +365,70 @@ async def test_engine_url_connection(): # Verify driver state assert driver.is_pool is True assert driver.conn is not None + + +def _driver_with_failing_cursor(error): + """A SqlDriver on a mock DbConnPool whose cursor raises `error` on execute.""" + cursor = AsyncMock() + cursor.execute.side_effect = error + + cursor_cm = MagicMock() + cursor_cm.__aenter__.return_value = cursor + + connection = MagicMock() + connection.cursor = MagicMock(return_value=cursor_cm) + + conn_cm = MagicMock() + conn_cm.__aenter__.return_value = connection + + pool = MagicMock() + pool.connection = MagicMock(return_value=conn_cm) + + db_pool = MagicMock(spec=DbConnPool) + db_pool.pool_connect.return_value = pool + db_pool._is_valid = True + + return SqlDriver(conn=db_pool), db_pool + + +@pytest.mark.asyncio +async def test_query_error_does_not_invalidate_pool(): + """Bad SQL must not poison the shared pool (issue #98). + + Marking the pool invalid on query-level errors made the next call tear + the pool down and rebuild it, churning pools on every malformed query. + """ + import psycopg + + driver, db_pool = _driver_with_failing_cursor(psycopg.errors.UndefinedTable('relation "missing" does not exist')) + + with pytest.raises(psycopg.errors.UndefinedTable): + await driver.execute_query("SELECT * FROM missing") + + assert db_pool._is_valid is True + + +@pytest.mark.asyncio +async def test_statement_timeout_does_not_invalidate_pool(): + """A canceled statement leaves the connection healthy; keep the pool.""" + import psycopg + + driver, db_pool = _driver_with_failing_cursor(psycopg.errors.QueryCanceled("canceling statement due to statement timeout")) + + with pytest.raises(psycopg.errors.QueryCanceled): + await driver.execute_query("SELECT pg_sleep(60)") + + assert db_pool._is_valid is True + + +@pytest.mark.asyncio +async def test_operational_error_invalidates_pool(): + """A connection-level failure must still mark the pool for rebuild.""" + import psycopg + + driver, db_pool = _driver_with_failing_cursor(psycopg.OperationalError("server closed the connection unexpectedly")) + + with pytest.raises(psycopg.OperationalError): + await driver.execute_query("SELECT 1") + + assert db_pool._is_valid is False From 2932bfebbeb60b9ca2521b365a8f59f980c4e7b7 Mon Sep 17 00:00:00 2001 From: Evan Culver Date: Wed, 5 Aug 2026 09:20:11 -0700 Subject: [PATCH 15/18] chore: bump Python deps and Debian base image past known CVEs (#2) uv lock --upgrade across the board, constraining mcp to <2 (2.0 removed mcp.server.fastmcp, which server.py is built on; 1.29.0 is the newest 1.x and satisfies every advisory). Base image bookworm -> trixie for both stages, and the runtime stage upgrades the base image's pip, which ships with known CVEs despite never being used at runtime. Headline bumps: aiohttp 3.11.16->3.14.3, cryptography 46.0.3->50.0.0, starlette 0.46.1->1.3.1, h11 0.14->0.16, pyjwt 2.10.1->2.13.0, urllib3 2.3.0->2.7.0, python-multipart 0.0.21->0.0.32. Test suite passes (206 passed, 9 skipped, 1 xfailed); streamable-http transport smoke-tested against the built image. Co-authored-by: Claude Fable 5 --- Dockerfile | 11 +- uv.lock | 2025 ++++++++++++++++++++++++++++------------------------ 2 files changed, 1102 insertions(+), 934 deletions(-) diff --git a/Dockerfile b/Dockerfile index 40b744d9..97d061d6 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,6 +1,6 @@ # First, build the application in the `/app` directory. # See `Dockerfile` for details. -FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder +FROM ghcr.io/astral-sh/uv:python3.12-trixie-slim AS builder ENV UV_COMPILE_BYTECODE=1 UV_LINK_MODE=copy # Disable Python downloads, because we want to use the system interpreter @@ -22,9 +22,9 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --frozen --no-dev -FROM python:3.12-slim-bookworm +FROM python:3.12-slim-trixie # It is important to use the image that matches the builder, as the path to the -# Python executable must be the same, e.g., using `python:3.11-slim-bookworm` +# Python executable must be the same, e.g., using `python:3.11-slim-trixie` # will fail. RUN groupadd -r app && useradd -r -g app app @@ -49,6 +49,11 @@ RUN apt-get update && apt-get install -y \ net-tools \ && rm -rf /var/lib/apt/lists/* +# pip is unused at runtime (the venv is prebuilt) but the base image's copy +# carries known CVEs. PATH resolves `python` to the pip-less venv interpreter, +# so name the system one. +RUN /usr/local/bin/python -m pip install --no-cache-dir --upgrade pip + COPY docker-entrypoint.sh /app/ RUN chmod +x /app/docker-entrypoint.sh diff --git a/uv.lock b/uv.lock index 046959a9..537029b8 100644 --- a/uv.lock +++ b/uv.lock @@ -1,19 +1,25 @@ version = 1 revision = 3 requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform != 'win32'", +] [[package]] name = "aiohappyeyeballs" -version = "2.6.1" +version = "2.7.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = 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"2026-07-20T02:07:43.461Z" }, ] From 7fac095823204bad6c7e7f3295a69094ab0e5da9 Mon Sep 17 00:00:00 2001 From: Jack Warren Date: Wed, 19 Aug 2026 15:58:41 -0400 Subject: [PATCH 16/18] Add timezone to ALLOWED_FUNCTIONS --- src/postgres_mcp/sql/safe_sql.py | 1 + tests/unit/sql/test_safe_sql.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/src/postgres_mcp/sql/safe_sql.py b/src/postgres_mcp/sql/safe_sql.py index 37382f0b..beeeebe5 100644 --- a/src/postgres_mcp/sql/safe_sql.py +++ b/src/postgres_mcp/sql/safe_sql.py @@ -478,6 +478,7 @@ class SafeSqlDriver(SqlDriver): "now", "statement_timestamp", "timeofday", + "timezone", "transaction_timestamp", # Additional Type Conversion "cast", diff --git a/tests/unit/sql/test_safe_sql.py b/tests/unit/sql/test_safe_sql.py index c55d2530..1f571d60 100644 --- a/tests/unit/sql/test_safe_sql.py +++ b/tests/unit/sql/test_safe_sql.py @@ -521,6 +521,8 @@ async def test_datetime_functions(safe_driver, mock_sql_driver): "SELECT now()", "SELECT statement_timestamp()", "SELECT timeofday()", + "SELECT timezone('UTC', timestamp with time zone '2001-02-16 20:38:40-05')", + "SELECT timestamp with time zone '2001-02-16 20:38:40-05' AT TIME ZONE 'America/Denver'", "SELECT transaction_timestamp()", ] From a3f5f1a945d8c85fa8b074f7e82b67b29789b2d2 Mon Sep 17 00:00:00 2001 From: xieguanyue Date: Thu, 27 Aug 2026 19:35:23 +0800 Subject: [PATCH 17/18] fix: Reset pooled connections to drop leaked session GUCs and HypoPG indexes Pooled backends reused by execute_query kept session GUCs after COMMIT and leftover HypoPG indexes after hypothetical-index analysis. Later MCP requests that hit the same PID inherited that state. Give AsyncConnectionPool a reset callback that runs DISCARD ALL outside a transaction and, if HypoPG is installed, schema-qualified hypopg_reset(). Cleanup errors propagate so the pool discards the connection. Combine DTA hypopg_create_index and hypopg_list_indexes into one checkout so size estimates still work after reset-on-return. Closes https://github.com/crystaldba/postgres-mcp/issues/203 --- src/postgres_mcp/index/dta_calc.py | 11 +- src/postgres_mcp/sql/sql_driver.py | 29 +++ tests/unit/index/test_dta_calc.py | 8 +- tests/unit/sql/test_pool_reset.py | 288 +++++++++++++++++++++++++++++ 4 files changed, 323 insertions(+), 13 deletions(-) create mode 100644 tests/unit/sql/test_pool_reset.py diff --git a/src/postgres_mcp/index/dta_calc.py b/src/postgres_mcp/index/dta_calc.py index c10e2e19..3f231134 100644 --- a/src/postgres_mcp/index/dta_calc.py +++ b/src/postgres_mcp/index/dta_calc.py @@ -163,19 +163,16 @@ async def generate_candidates(self, workload: list[tuple[str, SelectStmt, float] self.dta_trace(f"Filtered to {len(filtered_candidates)} after removing existing indexes.") self.dta_trace(f"Filtered to {len(condition_filtered1)} after removing unused columns.") self.dta_trace(f"Filtered to {len(condition_filtered)} after removing long text columns.") - # Batch create all hypothetical indexes and store their size estimates + # Batch create hypothetical indexes and read sizes in one checkout so + # pool reset cannot drop HypoPG state between statements (issue #203). if len(condition_filtered) > 0: query = "SELECT hypopg_create_index({});" * len(condition_filtered) - await SafeSqlDriver.execute_param_query( + query += "SELECT index_name, hypopg_relation_size(indexrelid) as index_size FROM hypopg_list_indexes;" + result = await SafeSqlDriver.execute_param_query( self.sql_driver, query, [idx.definition for idx in condition_filtered], ) - - # Get estimated sizes without resetting indexes yet - result = await self.sql_driver.execute_query( - "SELECT index_name, hypopg_relation_size(indexrelid) as index_size FROM hypopg_list_indexes;" - ) if result is not None: index_map = {r.cells["index_name"]: r.cells["index_size"] for r in result} for idx in condition_filtered: diff --git a/src/postgres_mcp/sql/sql_driver.py b/src/postgres_mcp/sql/sql_driver.py index 5beacb03..6b6c9b28 100644 --- a/src/postgres_mcp/sql/sql_driver.py +++ b/src/postgres_mcp/sql/sql_driver.py @@ -10,12 +10,40 @@ from urllib.parse import urlparse from urllib.parse import urlunparse +from psycopg import sql from psycopg.rows import dict_row from psycopg_pool import AsyncConnectionPool from typing_extensions import LiteralString logger = logging.getLogger(__name__) +_HYPOPG_SCHEMA_QUERY = """ +SELECT n.nspname +FROM pg_catalog.pg_extension e +JOIN pg_catalog.pg_namespace n ON n.oid = e.extnamespace +WHERE e.extname = 'hypopg' +""" + + +async def reset_pooled_connection(conn: Any) -> None: + """Reset session state before a pooled connection is reused. + + ``DISCARD ALL`` cannot run inside a transaction, and psycopg defaults to + autocommit=False, so this callback temporarily enables autocommit. Cleanup + errors propagate so psycopg_pool discards the connection. + """ + previous_autocommit = conn.autocommit + await conn.set_autocommit(True) + try: + await conn.execute("DISCARD ALL") + result = await conn.execute(_HYPOPG_SCHEMA_QUERY) + row = await result.fetchone() + if row: + schema = row["nspname"] if isinstance(row, dict) else row[0] + await conn.execute(sql.SQL("SELECT {schema}.hypopg_reset()").format(schema=sql.Identifier(schema))) + finally: + await conn.set_autocommit(previous_autocommit) + def obfuscate_password(text: str | None) -> str | None: """ @@ -91,6 +119,7 @@ async def pool_connect(self, connection_url: Optional[str] = None) -> AsyncConne min_size=1, max_size=5, open=False, # Don't connect immediately, let's do it explicitly + reset=reset_pooled_connection, ) # Open the pool explicitly diff --git a/tests/unit/index/test_dta_calc.py b/tests/unit/index/test_dta_calc.py index 5c9ef0f9..3a254c3a 100644 --- a/tests/unit/index/test_dta_calc.py +++ b/tests/unit/index/test_dta_calc.py @@ -134,9 +134,7 @@ async def test_generate_candidates(async_sql_driver, create_dta): } ) ], - # create index users.name - [MockCell({"indexrelid": 123})], - # pg_stat_statements + # hypopg_create_index + hypopg_list_indexes (one checkout; last statement) [MockCell({"index_name": "crystaldba_idx_users_name_1", "index_size": 81920})], # hypopg_reset [], @@ -523,9 +521,7 @@ async def test_basic_workload_analysis(async_sql_driver): } ), ], - # hypopg_create_index (for users.name, orders.user_id) - [MockCell({"indexrelid": 1554}), MockCell({"indexrelid": 1555})], - # hypopg_list_indexes + # hypopg_create_index + hypopg_list_indexes (one checkout; last statement) [ MockCell({"index_name": "crystaldba_idx_users_name_1", "index_size": 8000}), MockCell( diff --git a/tests/unit/sql/test_pool_reset.py b/tests/unit/sql/test_pool_reset.py new file mode 100644 index 00000000..3266e229 --- /dev/null +++ b/tests/unit/sql/test_pool_reset.py @@ -0,0 +1,288 @@ +"""Regression tests for pooled connection session-state reset (issue #203).""" + +import os +from typing import Any +from unittest.mock import AsyncMock +from unittest.mock import MagicMock +from unittest.mock import patch + +import pytest +from psycopg import sql + +from postgres_mcp.sql.sql_driver import DbConnPool +from postgres_mcp.sql.sql_driver import SqlDriver +from postgres_mcp.sql.sql_driver import reset_pooled_connection + +DATABASE_URI = os.environ.get("DATABASE_URI") + + +class _CursorCM: + async def __aenter__(self): + class _Cursor: + async def execute(self, query: object) -> None: + return None + + return _Cursor() + + async def __aexit__(self, exc_type, exc, tb): + return None + + +class _ConnectionCM: + async def __aenter__(self): + class _Connection: + def cursor(self): + return _CursorCM() + + return _Connection() + + async def __aexit__(self, exc_type, exc, tb): + return None + + +def _query_text(query: object) -> str: + if isinstance(query, str): + return query + return repr(query) + + +def _mock_connection(*, hypopg_schema: str | None = "public") -> tuple[MagicMock, list[object]]: + conn = MagicMock() + conn.autocommit = False + conn.set_autocommit = AsyncMock() + calls: list[object] = [] + + async def execute(query: object, *args: Any, **kwargs: Any) -> AsyncMock: + calls.append(query) + text = _query_text(query) + cursor = AsyncMock() + if "pg_extension" in text: + cursor.fetchone = AsyncMock(return_value=(hypopg_schema,) if hypopg_schema else None) + else: + cursor.fetchone = AsyncMock(return_value=None) + return cursor + + conn.execute = AsyncMock(side_effect=execute) + return conn, calls + + +@pytest.mark.asyncio +async def test_pool_connect_wires_reset_callback(): + captured: dict[str, Any] = {} + mock_pool = MagicMock() + mock_pool.open = AsyncMock() + mock_pool.close = AsyncMock() + + mock_pool.connection = MagicMock(return_value=_ConnectionCM()) + + def factory(*args: Any, **kwargs: Any) -> MagicMock: + captured["args"] = args + captured["kwargs"] = kwargs + return mock_pool + + with patch("postgres_mcp.sql.sql_driver.AsyncConnectionPool", side_effect=factory): + db_pool = DbConnPool("postgresql://user:pass@localhost/db") + await db_pool.pool_connect() + + assert captured["kwargs"]["reset"] is reset_pooled_connection + await db_pool.close() + + +@pytest.mark.asyncio +async def test_reset_runs_discard_all_outside_transaction(): + conn, calls = _mock_connection(hypopg_schema=None) + + await reset_pooled_connection(conn) + + assert conn.set_autocommit.await_args_list[0].args == (True,) + assert conn.set_autocommit.await_args_list[-1].args == (False,) + assert any(isinstance(q, str) and q.strip().upper() == "DISCARD ALL" for q in calls) + autocommit_true_index = next(i for i, c in enumerate(conn.set_autocommit.await_args_list) if c.args == (True,)) + restore_index = next(i for i, c in enumerate(conn.set_autocommit.await_args_list) if c.args == (False,)) + assert autocommit_true_index < restore_index + + +@pytest.mark.asyncio +async def test_reset_calls_schema_qualified_hypopg_reset_when_installed(): + conn, calls = _mock_connection(hypopg_schema="hypopg") + + await reset_pooled_connection(conn) + + reset_queries = [q for q in calls if "hypopg_reset" in _query_text(q)] + assert reset_queries + query = reset_queries[0] + assert isinstance(query, sql.Composed) + identifiers = [part for part in query if isinstance(part, sql.Identifier)] + assert identifiers + assert repr(identifiers[0]) == "Identifier('hypopg')" + + +@pytest.mark.asyncio +async def test_reset_skips_hypopg_when_not_installed(): + conn, calls = _mock_connection(hypopg_schema=None) + + await reset_pooled_connection(conn) + + assert not any("hypopg_reset" in _query_text(q) for q in calls) + + +@pytest.mark.asyncio +async def test_reset_errors_propagate_so_pool_can_discard(): + conn, _calls = _mock_connection(hypopg_schema=None) + conn.execute = AsyncMock(side_effect=RuntimeError("reset failed")) + + with pytest.raises(RuntimeError, match="reset failed"): + await reset_pooled_connection(conn) + + conn.set_autocommit.assert_awaited_with(False) + + +@pytest.mark.asyncio +async def test_failed_reset_discards_pooled_connection(): + from psycopg.pq import TransactionStatus + from psycopg_pool import AsyncConnectionPool + + closed: list[object] = [] + + class FakeConn: + def __init__(self) -> None: + self.pgconn = MagicMock() + self.pgconn.transaction_status = TransactionStatus.IDLE + self._pool = object() + + async def close(self) -> None: + closed.append(self) + + async def boom(_conn: object) -> None: + raise RuntimeError("intentional reset failure") + + pool = AsyncConnectionPool(conninfo="postgresql://unused", open=False, reset=boom) + fake = FakeConn() + try: + await pool._reset_connection(fake) # type: ignore[attr-defined] + assert closed == [fake] + assert fake._pool is None + finally: + await pool.close() + + +@pytest.mark.asyncio +@pytest.mark.skipif(not DATABASE_URI, reason="DATABASE_URI is not set") +async def test_session_guc_does_not_leak_across_checkouts(): + from psycopg_pool import AsyncConnectionPool + + async def first_row(driver: SqlDriver, query: str) -> dict[str, Any]: + rows = await driver.execute_query(query) # type: ignore[arg-type] + assert rows + return rows[0].cells + + pool = DbConnPool(DATABASE_URI) + raw_pool = AsyncConnectionPool( + conninfo=DATABASE_URI, + min_size=1, + max_size=1, + open=False, + reset=reset_pooled_connection, + ) + await raw_pool.open() + await raw_pool.wait() + pool.pool = raw_pool + pool._is_valid = True # type: ignore[attr-defined] + driver = SqlDriver(conn=pool) + try: + before = await first_row( + driver, + """ + SELECT pg_backend_pid() AS pid, + current_setting('enable_hashjoin') AS enable_hashjoin + """, + ) + changed = await first_row( + driver, + """ + SET enable_hashjoin = off; + SELECT pg_backend_pid() AS pid, + current_setting('enable_hashjoin') AS enable_hashjoin + """, + ) + after = await first_row( + driver, + """ + SELECT pg_backend_pid() AS pid, + current_setting('enable_hashjoin') AS enable_hashjoin + """, + ) + assert before["pid"] == changed["pid"] == after["pid"] + assert changed["enable_hashjoin"] == "off" + assert after["enable_hashjoin"] == before["enable_hashjoin"] + finally: + await pool.close() + + +@pytest.mark.asyncio +@pytest.mark.skipif(not DATABASE_URI, reason="DATABASE_URI is not set") +async def test_hypopg_state_does_not_leak_across_checkouts(): + from psycopg_pool import AsyncConnectionPool + + async def first_row(driver: SqlDriver, query: str) -> dict[str, Any]: + rows = await driver.execute_query(query) # type: ignore[arg-type] + assert rows + return rows[0].cells + + pool = DbConnPool(DATABASE_URI) + raw_pool = AsyncConnectionPool( + conninfo=DATABASE_URI, + min_size=1, + max_size=1, + open=False, + reset=reset_pooled_connection, + ) + await raw_pool.open() + await raw_pool.wait() + pool.pool = raw_pool + pool._is_valid = True # type: ignore[attr-defined] + driver = SqlDriver(conn=pool) + try: + installed = await first_row( + driver, + "SELECT EXISTS (SELECT 1 FROM pg_extension WHERE extname = 'hypopg') AS installed", + ) + if not installed["installed"]: + pytest.skip("HypoPG is not installed") + + await driver.execute_query("SELECT hypopg_reset(); SELECT hypopg_create_index('CREATE INDEX ON pg_class (relname)')") + after = await first_row(driver, "SELECT count(*) AS count FROM hypopg_list_indexes()") + assert int(after["count"]) == 0 + finally: + try: + await driver.execute_query("SELECT hypopg_reset()") + finally: + await pool.close() + + +@pytest.mark.asyncio +@pytest.mark.skipif(not DATABASE_URI, reason="DATABASE_URI is not set") +async def test_failed_reset_replaces_backend_when_database_available(): + from psycopg_pool import AsyncConnectionPool + + async def boom(conn: Any) -> None: + raise RuntimeError("intentional reset failure") + + raw_pool = AsyncConnectionPool( + conninfo=DATABASE_URI, + min_size=1, + max_size=1, + open=False, + reset=boom, + ) + await raw_pool.open() + await raw_pool.wait() + try: + async with raw_pool.connection() as conn: + pid_row = await (await conn.execute("SELECT pg_backend_pid()")).fetchone() + async with raw_pool.connection() as conn: + new_pid_row = await (await conn.execute("SELECT pg_backend_pid()")).fetchone() + assert pid_row is not None and new_pid_row is not None + assert pid_row[0] != new_pid_row[0] + finally: + await raw_pool.close() From b46e65907574f58009b99b5391ea212205cde3f5 Mon Sep 17 00:00:00 2001 From: cupskeee Date: Sun, 6 Sep 2026 16:14:48 +0700 Subject: [PATCH 18/18] fix(post-merge): reconcile cross-PR coherence (typing modernization + line length) Merging #173 (which removed the UP006/UP035 ruff ignores) together with PRs that still used legacy typing (`List`/`Dict`, `typing.List`) left the tree failing ruff. Applies #173's modernization to the stragglers (test_dta_calc.py, tests/utils.py, conftest.py, index_opt_base.py) and wraps three long tool-description strings from #160 (E501) via implicit string concatenation (text unchanged). ruff clean, pyright 0 errors, pytest 208 passed. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_01D6w353jusev2fgmWQY1mS1 --- src/postgres_mcp/index/index_opt_base.py | 2 +- src/postgres_mcp/server.py | 16 +++++++++++++--- tests/conftest.py | 2 +- tests/unit/index/test_dta_calc.py | 6 ++---- tests/utils.py | 5 ++--- 5 files changed, 19 insertions(+), 12 deletions(-) diff --git a/src/postgres_mcp/index/index_opt_base.py b/src/postgres_mcp/index/index_opt_base.py index 7ff00971..5f484251 100644 --- a/src/postgres_mcp/index/index_opt_base.py +++ b/src/postgres_mcp/index/index_opt_base.py @@ -3,10 +3,10 @@ import time from abc import ABC from abc import abstractmethod +from collections.abc import Iterable from dataclasses import dataclass from dataclasses import field from typing import Any -from typing import Iterable from pglast import parse_sql from pglast.ast import SelectStmt diff --git a/src/postgres_mcp/server.py b/src/postgres_mcp/server.py index 86793a91..3d4b88c6 100644 --- a/src/postgres_mcp/server.py +++ b/src/postgres_mcp/server.py @@ -111,7 +111,10 @@ async def list_schemas() -> ResponseType: @mcp.tool( - description="List tables, views, materialized views, sequences, or extensions in a PostgreSQL schema. Returns names and comments/descriptions for each object.", + description=( + "List tables, views, materialized views, sequences, or extensions in a PostgreSQL schema. " + "Returns names and comments/descriptions for each object." + ), annotations=ToolAnnotations( title="List Objects", readOnlyHint=True, @@ -229,7 +232,11 @@ async def list_objects( @mcp.tool( - description="Show columns (with types, nullability, defaults, and comments), constraints, indexes, and the table/view comment for a PostgreSQL object. Use this BEFORE writing queries to understand the schema.", + description=( + "Show columns (with types, nullability, defaults, and comments), constraints, indexes, and " + "the table/view comment for a PostgreSQL object. Use this BEFORE writing queries to " + "understand the schema." + ), annotations=ToolAnnotations( title="Get Object Details", readOnlyHint=True, @@ -430,7 +437,10 @@ async def get_object_details( @mcp.tool( - description="Explains the PostgreSQL execution plan for a SQL query, showing how the database will execute it and provides detailed cost estimates.", + description=( + "Explains the PostgreSQL execution plan for a SQL query, showing how the database will " + "execute it and provides detailed cost estimates." + ), annotations=ToolAnnotations( title="Explain Query", readOnlyHint=True, diff --git a/tests/conftest.py b/tests/conftest.py index e272d29b..14efd6ce 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,5 @@ import asyncio -from typing import Generator +from collections.abc import Generator import pytest from dotenv import load_dotenv diff --git a/tests/unit/index/test_dta_calc.py b/tests/unit/index/test_dta_calc.py index 3a254c3a..43f2f9c5 100644 --- a/tests/unit/index/test_dta_calc.py +++ b/tests/unit/index/test_dta_calc.py @@ -2,8 +2,6 @@ import json from logging import getLogger from typing import Any -from typing import Dict -from typing import Set from unittest.mock import AsyncMock from unittest.mock import MagicMock from unittest.mock import patch @@ -22,7 +20,7 @@ class MockCell: - def __init__(self, data: Dict[str, Any]): + def __init__(self, data: dict[str, Any]): self.cells = data @@ -684,7 +682,7 @@ async def test_replace_parameters_multiple(create_dta): ) # We'll need to return different values based on the context - def identify_column_side_effect(context, table_columns: Dict[str, Set[str]]): + def identify_column_side_effect(context, table_columns: dict[str, set[str]]): if "status =" in context: return ("users", "status") elif "amount BETWEEN" in context: diff --git a/tests/utils.py b/tests/utils.py index 7780cc29..c823548b 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1,9 +1,8 @@ import logging import os import time +from collections.abc import Generator from pathlib import Path -from typing import Generator -from typing import Tuple import docker import pytest @@ -12,7 +11,7 @@ logger = logging.getLogger(__name__) -def create_postgres_container(version: str) -> Generator[Tuple[str, str], None, None]: +def create_postgres_container(version: str) -> Generator[tuple[str, str], None, None]: """Create a PostgreSQL container of specified version and return its connection string.""" try: client = docker.from_env()