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MCP Crash Course

@Ayyappa054

代码仓库

关于 MCP Crash Course

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

基本信息

分类

AI 与智能体

运行时

python

传输方式

stdio

发布者

Ayyappa054

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

FAQ from MCP Crash Course

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.

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.

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.

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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