bhavesh-kalluru/First-MCP-server-in-Claude
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The Weather MCP Server is a simple Model Context Protocol server that demonstrates how to expose custom tools to an AI runtime using FastMCP.
🌤️ Weather MCP Server — Powered by FastMCP
A simple Model Context Protocol (MCP) server built with FastMCP that demonstrates how to expose custom tools to an AI runtime.
This example defines a single tool, get_weather, which returns mock weather data for a given location.
🚀 Features
- ✅ Minimal, easy-to-understand MCP server
- ⚙️ Implements a
get_weathertool - 🧩 Ready to integrate with MCP-compatible clients
- 🐍 Written in clean, modern Python
🧠 How It Works
The FastMCP class wraps boilerplate setup and communication for MCP stdio servers.
When the server starts, it exposes the get_weather function as a callable MCP tool that other agents or IDEs can invoke.
🧩 Code Overview
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("weather")
@mcp.tool()
def get_weather(location: str) -> str:
"""Get the current weather for a given location."""
return f"The current weather in {location} is sunny with a temperature of 25°C."
if __name__ == "__main__":
mcp.run()
⚡️ Getting Started
1️⃣ Clone the repo
bash
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git clone https://github.com/<your-username>/weather-mcp.git
cd weather-mcp
2️⃣ Create a virtual environment
bash
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uv venv
source .venv/bin/activate # macOS/Linux
3️⃣ Install dependencies
bash
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uv pip install 'mcp[cli]'
4️⃣ Run the server
bash
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uv run python main.py
If everything is configured correctly, the server will start and listen for MCP stdio connections.
🔍 Example Usage
You can connect this MCP server to any MCP-compatible client (e.g., an IDE, LLM runtime, or agent).
When prompted, provide:
Setting Value
Command uv
Arguments run --directory /path/to/weather-mcp python main.py
🧰 Project Structure
bash
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weather-mcp/
│
├── main.py # MCP server source
├── pyproject.toml # Project metadata (for uv/pdm)
└── README.md # You are here
💡 Future Improvements
Integrate with a real weather API (e.g., OpenWeatherMap)
Add error handling and temperature units
Extend to multi-location forecasting and alerts
👨💻 Author
Bhavesh Kalluru
AI Engineer | Generative AI | LLM Applications | MCP Integrations
📍 Looking for full-time opportunities in the U.S.