PostgreSQL Model Context Protocol (MCP) Server

A comprehensive Model Context Protocol (MCP) Server built with FastAPI that provides full PostgreSQL database operations through a standardized JSON-RPC 2.0 interface. This server can be integrated with VS Code and other MCP-compatible tools for seamless database management.

Features

🚀 Core MCP Functionality

• JSON-RPC 2.0 compliant API

• VS Code integration ready

• Authentication with API key

• Comprehensive error handling

• Auto-fallback to mock service when PostgreSQL is unavailable

🗄️ PostgreSQL Operations

Basic Operations

• Connection Testing - Test database connectivity and get server info

• Query Execution - Execute any SQL query (SELECT, INSERT, UPDATE, DELETE, etc.)

• Schema Information - Get complete database schema with tables and columns

• Table Information - Detailed table metadata, columns, indexes, and row counts

Advanced Operations

• Query Performance Analysis - EXPLAIN ANALYZE for query optimization

• Database Size Information - Database and table size statistics

• Table Statistics - Column statistics, null fractions, distinct values

• Active Connections - Monitor current database connections

• Locks Information - View database locks and blocking queries

• Slow Queries Analysis - Identify performance bottlenecks (requires pg_stat_statements)

Administrative Operations

• Table Backup - Create table copies with or without data

• Index Creation - Create indexes on tables (regular or unique)

• Table Optimization - VACUUM and ANALYZE tables for performance

Utility Operations

• Echo Tool - Simple echo for testing connectivity

Related MCP server: PostgreSQL MCP Server

Available Tools

Installation

1. Clone the repository:

git clone <repository-url>
cd pg-mcp

2. Install dependencies:

pip install -r requirements.txt
# or using pyproject.toml
pip install -e .

3. Configure PostgreSQL connection: Edit app/config.py with your PostgreSQL settings:

POSTGRES_DB_CONFIG = {
    "host": "localhost",
    "port": "5432", 
    "dbname": "your_database",
    "user": "your_username",
    "password": "your_password"
}

4. Set API key: Update MCP_API_KEY in app/config.py or set environment variable.

Usage

Start the Server

# Development
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# Production
uvicorn app.main:app --host 0.0.0.0 --port 8000

VS Code Integration

1. Install MCP extension in VS Code

2. Configure MCP settings in .vscode/mcp.json:

{
  "mcpServers": {
    "postgres-mcp": {
      "name": "PostgreSQL MCP Server",
      "url": "http://localhost:8000/mcp",
      "apiKey": "your-secret-mcp-api-key"
    }
  }
}

Direct API Usage

Test connection:

curl -X GET http://localhost:8000/mcp \
  -H "Authorization: Bearer your-secret-mcp-api-key"

Execute query:

curl -X POST http://localhost:8000/mcp \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-secret-mcp-api-key" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "postgres_query",
      "arguments": {
        "query": "SELECT version()"
      }
    }
  }'

Testing

Comprehensive Test Suite

# Run unit tests
pytest test/

# Run comprehensive integration test
python test_postgres_mcp_comprehensive.py

Individual Tool Testing

# Test specific functionality
python test_postgres_mcp.py

Architecture

pg-mcp/
├── app/
│   ├── main.py              # FastAPI application entry point
│   ├── mcp.py               # MCP server implementation
│   ├── postgres_service.py  # PostgreSQL service layer
│   ├── postgres_mock.py     # Mock service for testing
│   ├── json_rpc.py          # JSON-RPC 2.0 implementation
│   ├── auth.py              # Authentication middleware
│   ├── config.py            # Configuration settings
│   └── logger.py            # Logging configuration
├── test/                    # Test files
├── .vscode/
│   └── mcp.json            # VS Code MCP configuration
└── pyproject.toml          # Project dependencies

Key Features

🔒 Security

• API key authentication for all requests

• SQL injection protection through parameterized queries

• Error sanitization to prevent information leakage

🚀 Performance

• Connection pooling for database connections

• Async/await support throughout

• Query optimization tools and analysis

🛠️ Development

• Auto-reload support for development

• Comprehensive logging for debugging

• Mock service for testing without real database

• Type hints throughout codebase

🔧 Reliability

• Automatic fallback to mock service

• Comprehensive error handling

• Connection retry logic

• Graceful degradation

Examples

Query Execution

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "postgres_query",
    "arguments": {
      "query": "SELECT * FROM users WHERE active = $1",
      "params": ["true"]
    }
  }
}

Table Backup

{
  "jsonrpc": "2.0", 
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "postgres_backup_table",
    "arguments": {
      "source_table": "users",
      "backup_table": "users_backup_20240115",
      "schema": "public",
      "include_data": true
    }
  }
}

Index Creation

{
  "jsonrpc": "2.0",
  "id": 3, 
  "method": "tools/call",
  "params": {
    "name": "postgres_create_index",
    "arguments": {
      "table_name": "users",
      "column_names": ["email", "status"], 
      "index_name": "idx_users_email_status",
      "unique": false
    }
  }
}

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Ensure all tests pass

6. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

• Create an issue on GitHub

• Check the test files for usage examples

• Review the comprehensive test suite for full API coverage