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PostgreSQL MCP (PG-MCP) Server

@tanster1234

Repository

Basic information

Category

Databases

Transports

stdio

Author

tanster1234

About PostgreSQL MCP (PG-MCP) Server

No overview available yet

PostgreSQL MCP (PG-MCP) Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp_server_postgres": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

PostgreSQL MCP (PG-MCP) Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is PostgreSQL Model Context Protocol (PG-MCP) Server?

It is an MCP server for PostgreSQL databases that provides a comprehensive API for AI agents to discover, connect to, query, and understand PostgreSQL databases through MCP's resource-oriented architecture. It is built for AI agents and developers who need to integrate natural language interfaces with PostgreSQL.

How to use PostgreSQL Model Context Protocol (PG-MCP) Server?

Install via Docker or manual setup with Python 3.13+. Run python -m server.app after installing dependencies with uv sync --frozen. Clients connect to the SSE endpoint (default http://localhost:8000/sse) and invoke tools like connect, pg_query, and pg_explain using a secure connection ID.

Key features of PostgreSQL Model Context Protocol (PG-MCP) Server

  • Multi-database support with simultaneous connections.
  • Rich catalog information from table/column descriptions.
  • Query execution plan analysis via pg_explain tool.
  • Built-in extension context for PostGIS and pgvector.
  • Read-only mode enforced via transaction settings.
  • Connection pooling and secure opaque connection IDs.

Use cases of PostgreSQL Model Context Protocol (PG-MCP) Server

  • AI agents exploring database schema and sample data.
  • Natural language to SQL conversion using Claude CLI.
  • Automated database introspection for documentation or analysis.
  • Query performance analysis with execution plan tool.

Frequently asked questions

1

What are the runtime requirements?

Python 3.13+ and a PostgreSQL database. The server can be run via Docker or manually.

2

How do I connect a database?

Use the `connect` tool with a PostgreSQL connection string to obtain an opaque connection ID for subsequent queries.

3

Is the server read-only?

Yes, the server runs in read-only mode enforced via transaction settings.

4

What PostgreSQL extensions are supported?

Built-in context for PostGIS and pgvector is provided, and additional extensions can be added via YAML files.

5

How is security handled?

Connection details are never exposed in resource URLs; only opaque connection IDs are used. Credentials are sent only once during the initial connection.

Comments

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