一款用于 PostgreSQL 数据库的模型上下文协议（MCP）服务器，具备增强的 AI 代理功能。

关于PG-MCP项目的更多信息请见此处：

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

概述

PG-MCP 是 PostgreSQL 数据库模型上下文协议的服务器实现。它为 AI 代理提供了全面的 API，通过 MCP 的资源导向架构，发现、连接、查询和理解 PostgreSQL 数据库。

该实现在参考 Postgres MCP 实现基础上进行了若干关键改进：

1. 完整服务器实现：作为一个完整的服务器，带SSE传输，用于生产环境
2. 多数据库支持：同时连接多个PostgreSQL数据库
3. 丰富的目录信息：从数据库目录中提取并暴露表/列描述
4. 扩展上下文：提供关于PostgreSQL扩展如PostGIS和pgvector的详细基于YAML的知识
5. 查询解释：包含专门分析查询执行计划的工具
6. 稳健的连接管理：数据库连接的正确生命周期，并处理安全连接ID。

特色

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