广告合作
MCP.so
登录

MCP Crash Course

@Ayyappa054

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

Ayyappa054

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

MCP Crash Course 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"
      ]
    }
  }
}

MCP Crash Course 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

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

常见问题

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.

评论

AI 与智能体 分类下的更多 MCP 服务器

Transform MCP

Unstructured

The Unstructured Transform MCP Server connects your ADK agent to Unstructured, a document processing platform that turns raw files into structured, AI-ready data. This integration gives your agent the ability to parse PD

Magic Hour

Magic Hour MCP lets AI agents create and edit images, videos, and audio using Magic Hour’s hosted generation tools.

Ranla

Kurtis Tryber

AI growth agent for software businesses. Ranla ships pages that rank on Google, gets your product named in AI answers, replies on Reddit and sends lifecycle email. Ask how growth is going, have Ranla draft the work and a

L

Legion MCP

faulkj

MCP-native LLM councils for debates, juries, blind panels, voting, refinement, and custom multi-model deliberation.

1

localscreenshot - website screenshot mcp for ai agents

localscreenshot is a Website screenshot MCP server and API available at https://localscreenshot.com/api. Localscreenshot screenshot api turns any public web page into an image or a PDF. Take the capture from one of 227 c

广告合作