A
licenseB
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maintenanceA Model Context Protocol compatible server that provides weather information for any city using Ollama's LLM capabilities through an exposed get-weather tool.
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MIT
MCP Weather Server
Suggested as a replacement for the hardcoded geolocation functionality to get real coordinates for cities via the Google Maps API
Allows exposing the weather tools to LangChain agents for building workflows that can access location and weather data
Enables exposing the weather tools to OpenAI function-calling agents to incorporate weather data into conversations and decision-making
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Weather Server get the weather forecast for New York City"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🌦️ 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.
Related MCP server: ai-mcp
📁 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-server3. Run the Server
uv run weather.pyThis 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
Available Tools
2 toolsget_coordinatesC
Returns the latitude and longitude of the specified city as a tuple.
Args:
city: the city of
Inputs: city (required). Returns: result
get_forecastC
Get weather forecast for a location. Args: latitude: Latitude of the location longitude: Longitude of the location
Inputs: latitude (required), longitude (required). Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
B3.1/5.0
Scored across 2 tools
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count2/5
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.
Completeness2/5
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.
Related MCP Connectors
The official Model Context Protocol server for Ambee. It gives any MCP-compatible AI assistant — Claude, ChatGPT, Cursor, VS Code, Ollama, and more direct access to live air quality, pollen, and weather data. To get started, including information on signing up and obtaining your Ambee key, check out the Ambee documentation on https://docs.ambeedata.com
Hosted MCP server for Xweather weather data: conditions, forecasts, alerts, and more.
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
MCP server exposing the Backtest360 engine API as tools for AI agents.
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