MCPサーバー練習用コード

What is MCPサーバー練習用コード?

This is practice code for building a Model Context Protocol (MCP) server, as implemented in a corresponding Qiita article. It is intended for developers learning to create MCP servers using Python and the uv package manager, with an LLM API (e.g., Azure OpenAI Services) configured via a .env file.

How to use MCPサーバー練習用コード?

Clone the repository, create a .env file with your LLM API key (the author uses Azure OpenAI Services), then set up a Python virtual environment with uv venv, activate it, and install dependencies with uv pip install -e ..

Key features of MCPサーバー練習用コード

• Provides a foundational MCP server implementation for learning.
• Uses Python with uv for environment and dependency management.
• Integrates with LLM APIs via .env configuration (e.g., Azure OpenAI).
• Accompanied by a Qiita article for step‑by‑step guidance.

Use cases of MCPサーバー練習用コード

• Learning how to set up an MCP server from scratch.
• Experimenting with MCP protocol integration using Python.
• Practicing API‑based LLM connectivity in an MCP server context.

FAQ from MCPサーバー練習用コード

What LLM API does this server require?

The server expects an LLM API key stored in a .env file; the author uses Azure OpenAI Services, but other compatible APIs may work.

What are the runtime dependencies?

Python and the uv package manager are required. Dependencies are installed via uv pip install -e . after creating a virtual environment.

How do I set up the environment?

Clone the repository, copy .env.sample to .env, fill in your API details, then run uv venv, activate the virtual environment, and install dependencies.

Is this server ready for production?

No, this is practice code for learning MCP server development, as noted in the linked Qiita article.