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postgres mcp

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Basic Information

Postgres MCP Pro is an open source Model Context Protocol (MCP) server that exposes Postgres database capabilities to AI agents and developers throughout the development lifecycle. It is designed to do more than provide a simple database connection by offering deterministic, procedural tools for health checks, index tuning, query explain plans, schema inspection, and controlled SQL execution. The server supports both standard input/output and Server-Sent Events transports so it can be used with a variety of MCP clients. It can run via Docker or as a Python package and is intended to integrate with MCP-capable assistants to enable safe, context-aware SQL generation, workload analysis, and simulated index evaluation. Configuration supports unrestricted and restricted access modes and it can leverage Postgres extensions such as pg_stat_statements and hypopg for workload and planner simulation.

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App URL
https://github.com/crystaldba/postgres-mcp
Github Repository
https://github.com/crystaldba/postgres-mcp/blob/main/README.md

App Details

Features
Postgres MCP Pro bundles a suite of MCP tools that expose database capabilities to calling agents. Health checks include index health, buffer cache hit rates, vacuum and sequence checks, constraint and replication monitoring. Index tuning generates candidate multicolumn indexes, performs a guided search inspired by proven anytime algorithms, and simulates impact using hypopg. Query plan tools return EXPLAIN output and allow hypothetical index simulation. Schema intelligence tools provide object listings and detailed object information to help generate correct SQL. Protected SQL execution enforces restricted read-only mode with SQL parsing to reject dangerous statements. Transport options include stdio and SSE. The server uses an asynchronous psycopg3 client and provides Docker, pipx and source installation paths.
Use Cases
This project helps AI-assisted development and production maintenance by combining LLM-friendly interfaces with deterministic database tooling. It helps diagnose slow or resource-intensive queries, recommend and simulate index additions, and perform cost-benefit analysis for index choices. By exposing schema details and explain plans as MCP tools, it improves the accuracy and safety of LLM-generated SQL. Restricted execution mode and SQL parsing reduce risk when exposing databases to agents. The server aids developers in troubleshooting ORMs, optimizing application queries, and automating routine health checks. Experimental features let an LLM propose index configurations which are then validated by planner simulations, enabling hybrid workflows that pair generative reasoning with classical optimization.

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