Fast MCP

Overview of MCP Model Context Protocol (MCP) serves as a universal interface between Large Language Models and external data. While models like ChatGPT often live in isolated environments without network access, MCP acts as a standard connector. It allows an AI to understand how to call tools, format parameters, and interpret responses from your custom systems. Prerequisites To build an MCP server, you should possess a solid foundation in Python, specifically regarding asynchronous programming. Familiarity with JSON configuration files and basic REST API concepts is essential for implementing robust integrations. Key Libraries & Tools - **Fast MCP**: A high-level Python framework designed to streamline the creation of MCP servers. - **HTTPX**: A next-generation HTTP client for Python, used for making asynchronous API calls. - **FastAPI**: A modern web framework for building RESTful APIs that can be wrapped by MCP. - **YouTube Search**: A Python utility for querying video metadata. Code Walkthrough You can initialize a server using the `FastMCP` class. This server defines "tools" that the LLM can invoke. Below is a foundational implementation that exposes a search function. ```python from mcp.server.fastmcp import FastMCP Initialize the MCP server mcp = FastMCP("VideoSearch") @mcp.tool() def search_videos(query: str): """Search for videos based on keywords.""" # Logic to fetch data goes here return f"Results for {query}" ``` The `@mcp.tool()` decorator is vital; it generates the schema that tells the LLM exactly how to use this function. In a more advanced architecture, your MCP server should act as a thin client for an existing REST API to avoid logic duplication. ```python import httpx @mcp.tool() async def get_api_videos(query: str): async with httpx.AsyncClient() as client: response = await client.get(f"https://api.example.com/search?q={query}") return response.json() ``` Syntax Notes - **Docstrings**: MCP uses Python docstrings to explain tool functionality to the AI. Clear descriptions are mandatory. - **Type Hints**: Explicit typing (e.g., `query: str`) helps the MCP server generate the correct JSON schema for the LLM. Practical Examples Beyond searching for videos, MCP enables AI to interact with GitHub repositories, manage Stripe subscriptions, or query internal company databases directly through an interface like Claude Desktop. Tips & Gotchas Avoid direct function calls if you already have a REST API. Treating the MCP server as a separate "user" of your API ensures that bug fixes in the core logic propagate to your AI tools automatically. Always check your `config.json` pathing, as incorrect directory references are the primary cause of connection failures.