{"slug": "claude-code-mcp-a-practical-tutorial", "title": "Claude Code MCP: A Practical Tutorial", "summary": "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.", "body_md": "# Claude Code MCP: A Practical Tutorial\n\n[Claude](/en/tags/claude/)Code. I managed to get a custom server up and running in about 10 minutes using Python.\n\n## Project Setup\n\nI used `uv`\n\nfor this because it's significantly faster than standard pip for managing environments.\n\n```\nuv init word-count-mcp\ncd word-count-mcp\nuv add \"mcp[cli]\"\n```\n\n## Implementation\n\nThe 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.\n\nCreate `server.py`\n\n:\n\n``` python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"word-count\")\n\n@mcp.tool()\ndef word_count(text: str) -> dict:\n \"\"\"Count the words, characters, and lines in a block of text.\n\n Args:\n text: The text to analyze.\n \"\"\"\n words = text.split()\n return {\n \"words\": len(words),\n \"characters\": len(text),\n \"characters_no_spaces\": len(\"\".join(text.split())),\n \"lines\": len(text.splitlines()),\n }\n\nif __name__ == \"__main__\":\n mcp.run(transport=\"stdio\")\n```\n\n## Connecting to Claude Code\n\nSince 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:\n\n```\nclaude mcp add word-count -- uv run --directory \"$(pwd)\" server.py\n```\n\nThe command following the `--`\n\ntells Claude exactly how to execute the server. Once registered, the model can autonomously decide when to call the `word_count`\n\ntool 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.\n\n[Next Insight Compiler: Stopping Generic AI Advice →](/en/threads/3323/)", "url": "https://wpnews.pro/news/claude-code-mcp-a-practical-tutorial", "canonical_source": "https://promptcube3.com/en/threads/3333/", "published_at": "2026-07-25 19:01:33+00:00", "updated_at": "2026-07-25 19:05:28.012343+00:00", "lang": "en", "topics": ["ai-tools", "developer-tools", "ai-agents", "large-language-models"], "entities": ["Claude Code", "FastMCP", "Python", "Claude Code CLI"], "alternates": {"html": "https://wpnews.pro/news/claude-code-mcp-a-practical-tutorial", "markdown": "https://wpnews.pro/news/claude-code-mcp-a-practical-tutorial.md", "text": "https://wpnews.pro/news/claude-code-mcp-a-practical-tutorial.txt", "jsonld": "https://wpnews.pro/news/claude-code-mcp-a-practical-tutorial.jsonld"}}