MCP-Server
多服务器模型上下文协议(MCP)演示
一个完整的演示,展示如何构建和集成多个模型上下文协议(MCP)服务器,并与由 Groq 和实时 OpenWeatherMap 数据驱动的 LangGraph ReAct 代理协同工作。
该项目展示了 LLM 代理如何在一个统一的工作流中动态发现并使用来自不同 MCP 服务器和传输协议(stdio 和 streamable-http)的工具。
🏗️ 架构
flowchart LR
subgraph Agent["LangGraph ReAct Agent (client.py)"]
ChatGroq["LLM: Groq (Qwen / ChatGroq)"]
MultiMCP["MultiServerMCPClient"]
end
subgraph MathServer["Math MCP Server (mathserver.py)"]
MathTools["Tools: add, sub, mul, div"]
end
subgraph WeatherServer["Weather MCP Server (weatherserver.py)"]
WeatherTools["Tool: get_weather (OpenWeatherMap API)"]
end
MultiMCP -- "stdio transport (subprocess)" --> MathServer
MultiMCP -- "streamable-http (http://127.0.0.1:8000/mcp)" --> WeatherServer组件
数学 MCP 服务器(
mathserver.py):使用 FastMCP 构建。
通过
stdio传输运行。公开算术工具:
add、sub、mul、div。由 MCP 客户端自动作为后台子进程启动。
天气 MCP 服务器(
weatherserver.py):使用 FastMCP 构建,通过
streamable-http传输在http://127.0.0.1:8000/mcp上运行。使用
httpx从 OpenWeatherMap API 获取实时气象数据。公开
get_weather工具(返回温度、天气描述、体感温度和湿度)。
编排客户端(
client.py):使用
langchain-mcp-adapters中的MultiServerMCPClient同时连接到两个 MCP 服务器。组装工具,并使用 Groq 的快速 LLM 推理(
ChatGroq)驱动 LangGraph ReAct 代理。
Related MCP server: AIE8-MCP Server
📁 项目结构
Mcp_demo/
├── client.py # ReAct Agent & Multi-Server MCP client orchestrator
├── mathserver.py # FastMCP server running on stdio transport
├── weatherserver.py # FastMCP server with OpenWeatherMap API on HTTP transport
├── requirements.txt # Python package dependencies
├── pyproject.toml # Project configuration & metadata
├── .env.example # Environment variables template
├── .gitignore # Git ignore rules (protects .env and caches)
└── README.md # Project documentation🚀 快速开始
1. 前提条件
Python 3.10+
OpenWeatherMap API 密钥 (免费层级)
2. 安装
克隆仓库并安装依赖:
git clone https://github.com/sreeshanthkprakash-stack/MCP-Server.git
cd MCP-Server
# Create and activate virtual environment
python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On Linux/macOS:
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt3. 环境配置
在根目录下创建一个 .env 文件(或从 .env.example 复制):
GROQ_API_KEY=your_groq_api_key_here
OPENWEATHER_API_KEY=your_openweather_api_key_here🧪 运行应用程序
步骤 1:启动天气 MCP 服务器
由于天气服务器通过 HTTP 通信,请先在终端中启动它:
python weatherserver.py天气服务器将开始监听
http://127.0.0.1:8000/mcp。
步骤 2:运行代理客户端
打开第二个终端窗口(确保虚拟环境已激活),然后运行:
python client.py预期输出示例
math_response The result of the expression ((15*20)+200)/4 is 125.
weather_response Weather in Hyderabad: haze, temperature 29.5°C (feels like 32.1°C), humidity 65%📦 依赖项
mcp:官方模型上下文协议 SDK(FastMCP)langchain-mcp-adapters:将 LangChain 连接到 MCP 服务器langgraph:基于图的代理编排运行时langchain-groq:使用 Groq 的高速 LLM 推理httpx:用于天气 API 请求的异步 HTTP 客户端python-dotenv:安全的环境变量加载
📄 许可证
本项目是开源的,并根据 MIT 许可证 提供。
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