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MCP verified MIT Self-run

Pg Mcp Server

mcp-stuzero-pg-mcp-server · by stuzero

MCP server from stuzero/pg-mcp-server.

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Install

$ agentstack add mcp-stuzero-pg-mcp-server

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

Passed review? Show it. Paste this badge into your README, it links to the public security report.

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
11mo ago

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

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

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.