MCP Python SDK - A Python implementation of the Model Context Protocol for secure AI-data communication.

## MCP Python SDK Overview The MCP Python SDK is an official software development kit for implementing servers and clients based on the Model Context Protocol (MCP). It facilitates secure communication between large language models (LLMs) and external data sources, such as files, browsers, and web services. The SDK is designed to simplify the development of MCP-compatible applications in Python. ## Key Features of MCP Python SDK The MCP Python SDK offers the following key features: - **Client and Server Building**: Enables developers to create MCP clients and servers. - **Standard Transports**: Supports stdio and Server-Sent Events (SSE) for communication. - **Protocol Handling**: Manages all MCP protocol messages and lifecycle events. - **Resource and Tool Exposure**: Provides functionality through Resources (similar to GET endpoints) and Tools (similar to POST endpoints). - **Prompt Definition**: Allows the creation of reusable interaction templates. ## AI Application Support The MCP Python SDK supports AI applications by enabling secure and authorized access to local or remote data sources. It separates context provision from LLM interaction, enhancing security and usability. Developers can integrate tools, resources, and prompts to extend functionality, such as calculating BMI, fetching weather data, or reviewing code. ## Key Features of MCP Python SDK The MCP Python SDK includes the following functions: - **Tool Addition**: Developers can add tools like `calculate_bmi` or `fetch_weather`. - **Resource Management**: Supports adding resources like `get_config` or `get_user_profile`. - **Prompt Creation**: Allows defining prompts such as `review_code` or `debug_error`. - **Image Handling**: Includes an `Image` class for processing image data. - **Lifecycle Management**: Uses a lifespan API for application lifecycle control. - **Integration**: Works with Claude Desktop and ASGI servers for broader deployment. ## Installation and Usage Developers can install and use the MCP Python SDK as follows: - **Installation**: Use `uv add "mcp[cli]"` for uv-managed projects or `pip install mcp` for traditional Python environments. - **Development Tools**: Run tools with `uv run mcp` or `mcp dev server.py`. - **Server Operations**: Start servers with commands like `mcp install server.py`, `python server.py`, or `mcp run server.py`. - **Testing**: Use `mcp dev server.py --with pandas --with numpy` for integration testing. - **ASGI Mounting**: Mount to ASGI servers using the `sse_app()` method. ## Project Resources The MCP Python SDK and its documentation are available at the following URLs: - **GitHub Repository**: [https://github.com/modelcontextprotocol/python-sdk](https://github.com/modelcontextprotocol/python-sdk) - **PyPI Page**: [https://pypi.org/project/mcp/](https://pypi.org/project/mcp/) - **Official MCP Website**: [https://modelcontextprotocol.io](https://modelcontextprotocol.io) - **Specification**: [https://spec.modelcontextprotocol.io](https://spec.modelcontextprotocol.io) ## Comparison with Other MCP SDKs Compared to other MCP SDKs like the Kotlin or Java SDKs, the MCP Python SDK is optimized for Python-centric environments. It integrates seamlessly with popular AI tools like Claude Desktop and ASGI servers, leveraging Python's widespread adoption in AI development. Its ease of use and extensive ecosystem support make it a preferred choice for Python developers. ## Community and Ecosystem The MCP Python SDK benefits from active community engagement, including discussions on [GitHub Discussions](https://github.com/modelcontextprotocol/python-sdk/discussions). Reference implementations and additional resources are available in the [Servers Repository](https://github.com/modelcontextprotocol/servers), showcasing its versatility and extensibility. ## Purpose of Model Context Protocol The Model Context Protocol (MCP) is designed to establish secure, authorized communication between large language models (LLMs) and external data sources. It enables AI applications to safely access local or remote data, such as files, browsers, and web services, while separating context provision from LLM interaction for enhanced security and usability. ### Citation sources: - [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - Official URL Updated: 2025-04-01