A Model Context Protocol server implementation in Python that provides access to Microsoft SQL Server databases. This server enables Language Models to inspect table schemas and execute SQL queries through a standardized interface.

• Asynchronous operation using Python's asyncio
• Environment-based configuration using python-dotenv
• Comprehensive logging system
• Connection pooling and management via pyodbc
• Error handling and recovery
• FastAPI integration for API endpoints
• Pydantic models for data validation
• MSSQL connection handling with ODBC Driver

• Python 3.x
• Required Python packages:
  • pyodbc
  • pydantic
  • python-dotenv
  • mcp-server
• ODBC Driver 17 for SQL Server

git clone https://github.com/amornpan/py-mcp-mssql.git cd py-mcp-mssql pip install -r requirements.txt

The screenshot above demonstrates the server being used with Claude to analyze and visualize SQL data.

PY-MCP-MSSQL/ ├── src/ │ └── mssql/ │ ├── __init__.py │ └── server.py ├── tests/ │ ├── __init__.py │ ├── test_mssql.py │ └── test_packages.py ├── .env ├── .env.example ├── .gitignore ├── README.md └── requirements.txt 

• src/mssql/ - Main source code directory
  • __init__.py - Package initialization
  • server.py - Main server implementation
• tests/ - Test files directory
  • __init__.py - Test package initialization
  • test_mssql.py - MSSQL functionality tests
  • test_packages.py - Package dependency tests
• .env - Environment configuration file (not in git)
• .env.example - Example environment configuration
• .gitignore - Git ignore rules
• README.md - Project documentation
• requirements.txt - Project dependencies

Create a .env file in the project root:

MSSQL_SERVER=your_server MSSQL_DATABASE=your_database MSSQL_USER=your_username MSSQL_PASSWORD=your_password MSSQL_DRIVER={ODBC Driver 17 for SQL Server}

@app.list_resources() async def list_resources() -> list[Resource]

• Lists all available tables in the database
• Returns table names with URIs in the format mssql://<table_name>/data
• Includes table descriptions and MIME types

@app.read_resource() async def read_resource(uri: AnyUrl) -> str

• Reads data from specified table
• Accepts URIs in the format mssql://<table_name>/data
• Returns first 100 rows in CSV format
• Includes column headers

@app.call_tool() async def call_tool(name: str, arguments: dict) -> list[TextContent]

• Executes SQL queries
• Supports both SELECT and modification queries
• Returns results in CSV format for SELECT queries
• Returns affected row count for modification queries

Add to your Claude Desktop configuration:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{ "mcpServers": { "mssql": { "command": "python", "args": [ "server.py" ], "env": { "MSSQL_SERVER": "your_server", "MSSQL_DATABASE": "your_database", "MSSQL_USER": "your_username", "MSSQL_PASSWORD": "your_password", "MSSQL_DRIVER": "{ODBC Driver 17 for SQL Server}" } } } }

The server implements comprehensive error handling for:

• Database connection failures
• Invalid SQL queries
• Resource access errors
• URI validation
• Tool execution errors

All errors are logged and returned with appropriate error messages.

• Environment variable based configuration
• Connection string security
• Result set size limits
• Input validation through Pydantic
• Proper SQL query handling

Feel free to reach out to me if you have any questions about this project or would like to collaborate!

Made with ❤️ by Amornpan Phornchaicharoen

This project is licensed under the MIT License - see the LICENSE file for details.

Amornpan Phornchaicharoen

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Create a requirements.txt file with:

fastapi>=0.104.1 pydantic>=2.10.6 uvicorn>=0.34.0 python-dotenv>=1.0.1 pyodbc>=4.0.35 anyio>=4.5.0 mcp==1.2.0 

These versions have been tested and verified to work together. The key components are:

• fastapi and uvicorn for the API server
• pydantic for data validation
• pyodbc for SQL Server connectivity
• mcp for Model Context Protocol implementation
• python-dotenv for environment configuration
• anyio for asynchronous I/O support

• Microsoft SQL Server team for ODBC drivers
• Python pyodbc maintainers
• Model Context Protocol community
• Contributors to the python-dotenv project