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jeannassereldine

MCP Weather Server

by jeannassereldine
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Python
Hybrid

🌦️ MCP Weather Server

A simple and modular MCP (Modular Command Protocol) server that exposes weather-related tools — perfect for integration with AI agents, LLMs, or any tool-using client.

This project demonstrates how to create and serve tools such as:

  • get_coordinates(city)

  • get_forecast(latitude, longitude)

Designed to be lightweight, clean, and easy to extend.

🧠 What Is MCP?

MCP (Modular Command Protocol) is a protocol for exposing tools (Python functions) in a machine-readable format so they can be:

  • Automatically discovered

  • Dynamically called by AI agents

  • Interoperable across systems

It’s built for tool-using LLMs, agents, and next-gen integrations.

📁 Project Structure

mcp-server/
├── main.py           # Starts the FastMCP server
├── tools   
|------ get_forcast.py         # MCP tools: get_coordinates and get_forecast
├── pyproject.toml   # Python dependencies
└── README.md         # You're here!

🚀 Getting Started

1. Clone the Repo

git clone https://github.com/jeannassereldine/mcp-server.git
cd mcp-server

3. Run the Server

uv run weather.py

This starts the MCP server over stdio. You can connect any MCP client that supports the protocol.

🔧 Tools Overview

get_coordinates(city: str) -> Tuple[float, float]

Returns hardcoded latitude and longitude for a given city.

✅ Replace this with a real geolocation API like OpenCage or Google Maps.

get_forecast(latitude: float, longitude: float) -> str

Returns a formatted weather forecast string for the given coordinates.

✅ Replace with a live weather API like api.weather.gov.

format_forecast(forecasts: List[Dict]) -> str

Helper function that formats multiple forecast entries into a readable string.

🧩 Want to Build an MCP Client?

Stay tuned! The next part of this project will include a lightweight client that can:

  • Auto-discover tools

  • Call them based on context

  • Build real-time agent workflows

🧠 Use Cases

  • Build agent backends with clean, callable tools

  • Expose local or cloud-based APIs to LLMs

  • Prototype tools for LangChain or OpenAI function-calling agents

  • Teach MCP integration through a practical example

📌 License

This project is open-source under the MIT License.

👋 Contributing

Pull requests are welcome! Feel free to open issues or suggest features you'd like to see.

🔗 Related