SampleMCP
A demonstration MCP server that provides math (add/multiply) and weather tools, connecting via stdio and streamable HTTP, and integrates with LangChain and LangGraph for agentic workflows.
README
SampleMCP
A simple Python project demonstrating how to build and connect MCP servers using FastMCP, LangChain MCP adapters, LangGraph, and OpenAI.
Features
- Math MCP server using
stdio - Weather MCP server using
streamable-http - LangChain MCP client connecting to multiple MCP servers
- LangGraph ReAct agent using MCP tools
- Example tool calls for math and weather queries
Project Structure
SampleMCP/
├── client.py # MCP client + LangGraph agent
├── mathserver.py # Math MCP server with add/multiply tools
├── weather.py # Weather MCP server using streamable HTTP
├── main.py # Basic sample entry point
├── pyproject.toml # Project metadata and dependencies
├── requirements.txt # Python dependencies
└── README.md
Requirements
- Python 3.12+
- OpenAI API key
- Optional: Groq API key
Installation
Clone the repository:
git clone https://github.com/JayantPrakash/SampleMCP.git
cd SampleMCP
Install dependencies:
pip install -r requirements.txt
Or using uv:
uv sync
Environment Variables
Create a .env file in the project root:
OPENAI_API_KEY=your_openai_api_key
GROQ_API_KEY=your_groq_api_key
Running the MCP Servers
1. Start the Weather Server
The weather server uses streamable-http.
python weather.py
By default, it exposes the MCP endpoint at:
http://localhost:8000/mcp
2. Math Server
The math server uses stdio and is started automatically by the client through:
"command": "python",
"args": ["mathserver.py"],
"transport": "stdio"
Running the Client
In a separate terminal, run:
python client.py
The client connects to:
mathserver.pythroughstdioweather.pythroughstreamable_http
It then creates a LangGraph ReAct agent and asks:
what's (3 + 5) x 12?
and:
what is the weather in California?
MCP Tools
Math Server
Defined in mathserver.py.
add(a: int, b: int) -> int
Adds two numbers.
multiple(a: int, b: int) -> int
Multiplies two numbers.
Weather Server
Defined in weather.py.
get_weather(location: str) -> str
Returns a sample weather response for a given location.
Notes
mathserver.pyusestransport="stdio".weather.pyusestransport="streamable-http".- In
client.py, the streamable HTTP transport is configured asstreamable_http. - Make sure the weather server is running before executing
client.py.
Example Output
Math response: 96
Weather response: It's always raining in California
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。