Claude Code MCP: A Practical Tutorial

Claude Code MCP enables developers to build custom AI agent tools in about 10 minutes using Python, according to a practical tutorial. The tutorial demonstrates creating a word-count server with the FastMCP SDK and connecting it to the Claude Code CLI via a single command, allowing the model to autonomously call the tool based on user prompts.

Claude Code MCP: A Practical Tutorial Claude /en/tags/claude/ Code. I managed to get a custom server up and running in about 10 minutes using Python. Project Setup I used uv for this because it's significantly faster than standard pip for managing environments. uv init word-count-mcp cd word-count-mcp uv add "mcp cli " Implementation The official SDK makes the deployment straightforward. You just need a single file to define your logic. The key is using type hints and docstrings; the SDK uses these to generate the schema that tells the LLM exactly how to use the tool. Create server.py : python from mcp.server.fastmcp import FastMCP mcp = FastMCP "word-count" @mcp.tool def word count text: str - dict: """Count the words, characters, and lines in a block of text. Args: text: The text to analyze. """ words = text.split return { "words": len words , "characters": len text , "characters no spaces": len "".join text.split , "lines": len text.splitlines , } if name == " main ": mcp.run transport="stdio" Connecting to Claude Code Since this is a stdio-based server, it stays idle until a client connects via stdin/stdout. To link this to your AI workflow, register it directly within the Claude Code CLI: claude mcp add word-count -- uv run --directory "$ pwd " server.py The command following the -- tells Claude exactly how to execute the server. Once registered, the model can autonomously decide when to call the word count tool based on the user's prompt. This is a highly efficient way to build a custom LLM agent without the overhead of managing complex API endpoints. Next Insight Compiler: Stopping Generic AI Advice → /en/threads/3323/