MCP Weather Server

🌦️ 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.

📄 Original article on LinkedIn ← (https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7349014151165313025)

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A lightweight server that exposes weather-related tools (get_coordinates and get_forecast) using the Modular Command Protocol, designed for integration with AI agents and LLMs.

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