MCP Python SDK: Build AI Tool Servers with Python

The MCP Python SDK brings the power of Model Context Protocol to the Python ecosystem. Build custom tool servers that leverage Python's rich library ecosystem for data science, automation, and enterprise integration.

Overview

The Python SDK provides async-first implementations of MCP servers and clients. Built on FastMCP for simplified development, it makes creating Python-based tools as easy as writing decorated functions.

Key Features

• FastMCP — Simplified high-level API for rapid server development
• Async Native — Built on asyncio for efficient concurrent tool execution
• Type Hints — Full type annotation support with Pydantic validation
• Decorator Pattern — Define tools as simple decorated Python functions
• Multiple Transports — stdio, SSE, and streamable HTTP

Getting Started

pip install mcp

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("my-tools")

@mcp.tool()
def analyze_data(query: str) -> str:
    """Analyze data based on a query."""
    # Your analysis logic here
    return f"Analysis results for: {query}"

mcp.run()

Use Cases

• Data Science Tools — Pandas, NumPy, scikit-learn wrapped as MCP tools
• Database Integration — SQLAlchemy-powered database access tools
• API Connectors — Python requests-based API integrations
• ML Inference — Expose ML models through MCP for agent use

Best Practices

• Use type hints — They serve as both documentation and validation schemas
• Write docstrings — Tool docstrings become the AI-facing descriptions
• Handle async properly — Use async for I/O-bound operations
• Package with uv — Use uv for reproducible, fast package management

Frequently Asked Questions

Conclusion

Stay ahead of the curve by exploring our comprehensive directories. Browse the AI Agent directory with 400+ agents and the MCP Server directory with 2,300+ servers to find the perfect tools for your workflow.