🌦️ 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

🚀 Getting Started

1. Clone the Repo

3. Run the Server

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

• 📄 Original article on LinkedIn ← (