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MCP Crash Course

@Ayyappa054

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基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

Ayyappa054

MCP Crash Course について

A practical demonstration of integrating LangChain with Model Control Protocol (MCP) featuring both single and multi-server implementations. Includes examples of mathematical computations and weather queries using async operations, React agents, and OpenAI integration. Perfect fo

#langchain#mcp#integration#math#weather

MCP Crash Course の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "langchain-mcp-examples": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

MCP Crash Course のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP Crash Course?

A demonstration project showcasing integration of LangChain with Model Context Protocol (MCP) adapters. It implements a system that handles mathematical calculations and weather queries through separate MCP servers. Designed for developers learning MCP and LangChain.

How to use MCP Crash Course?

Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, and add your OpenAI API key to a .env file. Then run python main.py for the single‑server example or python langchain_client.py for the multi‑server example.

Key features of MCP Crash Course

  • Integrates LangChain with MCP adapters
  • Multiple MCP servers (math and weather)
  • Async operation support
  • Environment variable configuration
  • Both single‑server and multi‑server usage examples

Use cases of MCP Crash Course

  • Learning how to integrate MCP with LangChain
  • Demonstrating math and weather queries via separate MCP servers
  • Prototyping multi‑server agent systems
  • Teaching MCP stdio client and MultiServerMCPClient usage
  • Reference for building custom MCP adapter examples

よくある質問

1

What are the runtime requirements?

Python 3.x and an OpenAI API key are required. Dependencies include `langchain-core`, `langchain-openai`, `langchain-mcp-adapters`, `langgraph`, `python-dotenv`, and `mcp`.

2

How do the two main scripts differ?

`main.py` uses a stdio client to connect to a single math server. `langchain_client.py` uses `MultiServerMCPClient` to manage both a math server and a weather server, allowing the agent to choose the appropriate tool.

3

Does MCP Crash Course require external services?

Yes. The LangChain agent uses OpenAI’s API, so a valid `OPENAI_API_KEY` must be set in the `.env` file.

4

What transport does the project use?

The single‑server example uses stdio client communication. The multi‑server example uses MCP’s `MultiServerMCPClient`, though the exact transport is not explicitly stated.

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