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
```bash
git clone https://github.com/jeannassereldine/mcp-server.git
cd mcp-server
```
### 3. Run the Server
```bash
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](https://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](#) ← *(https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7349014151165313025)*
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: get_coordinates converts a city name to geographic coordinates, while get_forecast provides weather data for given coordinates. There is no overlap or ambiguity between these functions.
Both tools follow a consistent 'verb_noun' naming pattern (get_coordinates, get_forecast) with identical verb style and snake_case convention throughout. The naming is perfectly uniform and predictable.
With only 2 tools, this server feels severely under-scoped for a weather domain. A weather server should typically include current conditions, forecasts, historical data, alerts, and multiple location input methods. Two tools cannot provide meaningful coverage.
The tool surface is severely incomplete for a weather server. Missing essential operations like getting current weather conditions, temperature data, precipitation forecasts, weather alerts, or supporting direct city name input for forecasts. The workflow requires manual coordinate lookup before getting forecasts, creating unnecessary friction.