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PostgreSQL Model Context Protocol (PG-MCP) Server

A Model Context Protocol (MCP) server for PostgreSQL databases with enhanced capabilities for AI agents.

Overview

PG-MCP is a server implementation of the Model Context Protocol for PostgreSQL databases. It provides a comprehensive API for AI agents to discover, connect to, query, and understand PostgreSQL databases through MCP’s resource-oriented architecture.

This implementation builds upon and extends the reference Postgres MCP implementation with several key enhancements:

Full Server Implementation: Built as a complete server with SSE transport for production use
Multi-database Support: Connect to multiple PostgreSQL databases simultaneously
Rich Catalog Information: Extracts and exposes table/column descriptions from the database catalog
Extension Context: Provides detailed YAML-based knowledge about PostgreSQL extensions like PostGIS and pgvector
Query Explanation: Includes a dedicated tool for analyzing query execution plans
Robust Connection Management: Proper lifecycle for database connections with secure connection ID handling

Features

Connection Management

Connect Tool: Register PostgreSQL connection strings and get a secure connection ID
Disconnect Tool: Explicitly close database connections when done
Connection Pooling: Efficient connection management with pooling

Query Tools

pg_query: Execute read-only SQL queries using a connection ID
pg_explain: Analyze query execution plans in JSON format

Schema Discovery Resources

List schemas with descriptions
List tables with descriptions and row counts
Get column details with data types and descriptions
View table constraints and indexes
Explore database extensions

Data Access Resources

Sample table data (with pagination)
Get approximate row counts

Extension Context

Built-in contextual information for PostgreSQL extensions like:

PostGIS: Spatial data types, functions, and examples
pgvector: Vector similarity search functions and best practices

Additional extensions can be easily added via YAML config files.

Installation

Prerequisites

Python 3.13+
PostgreSQL database(s)

Using Docker

# Clone the repository
git clone https://github.com/stuzero/pg-mcp.git
cd pg-mcp

# Build and run with Docker Compose
docker-compose up -d
Manual Installation

# Clone the repository
git clone https://github.com/stuzero/pg-mcp.git
cd pg-mcp

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venvScriptsactivate

# Install using uv
uv sync –frozen

# Run the server
python -m server.app
Usage

Testing the Server

The repository includes test scripts to verify server functionality:

# Basic server functionality test
python test.py “postgresql://username:password@hostname:port/database”

# Claude-powered natural language to SQL conversion
python client/claude_cli.py “Show me the top 5 customers by total sales”
The claude_cli.py script requires environment variables:

# .env file
DATABASE_URL=postgresql://username:password@hostname:port/database
ANTHROPIC_API_KEY=your-anthropic-api-key
PG_MCP_URL=http://localhost:8000/sse

For AI Agents

Example prompt for use with agents:

Use the PostgreSQL MCP server to analyze the database.
Available tools:
– connect: Register a database connection string and get a connection ID
– disconnect: Close a database connection
– pg_query: Execute SQL queries using a connection ID
– pg_explain: Get query execution plans

You can explore schema resources via:
pgmcp://{conn_id}/schemas
pgmcp://{conn_id}/schemas/{schema}/tables
pgmcp://{conn_id}/schemas/{schema}/tables/{table}/columns

Architecture

This server is built on:

MCP: The Model Context Protocol foundation
FastMCP: Python library for MCP
asyncpg: Asynchronous PostgreSQL client
YAML: For extension context information

Security Considerations

The server runs in read-only mode by default (enforced via transaction settings)
Connection details are never exposed in resource URLs, only opaque connection IDs
Database credentials only need to be sent once during the initial connection

Contributing

Contributions are welcome! Areas for expansion:

Additional PostgreSQL extension context files
More schema introspection resources
Query optimization suggestions