PostgreSQL Model Context Protocol (PG-MCP) Server

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

More info on the pg-mcp project here:

https://stuzero.github.io/pg-mcp/

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:

1. Full Server Implementation: Built as a complete server with SSE transport for production use

2. Multi-database Support: Connect to multiple PostgreSQL databases simultaneously

3. Rich Catalog Information: Extracts and exposes table/column descriptions from the database catalog

4. Extension Context: Provides detailed YAML-based knowledge about PostgreSQL extensions like PostGIS and pgvector

5. Query Explanation: Includes a dedicated tool for analyzing query execution plans

6. 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-server.git
cd pg-mcp-server

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

Manual Installation

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

# Install dependencies and create a virtual environment ( .venv )
uv sync

# Activate the virtual environment
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 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 example-clients/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

A comprehensive database description is available at this resource:
pgmcp://{conn_id}/

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