MCP Weather Server

Minimal MCP server that exposes weather data via tools using the Open-Meteo API (no API key required).

Architecture

The project has three parts: a client (Cursor or the Python test client), the MCP server (this repo), and the Open-Meteo API. The client and server talk over stdio (stdin/stdout); the server talks to the API over HTTPS.

flowchart LR
  subgraph local [Your machine]
    Client[MCP Client]
    Server[MCP Server]
  end
  API[Open-Meteo API]
  Client <-->|"stdio (JSON-RPC)"| Server
  Server -->|"HTTPS GET"| API

How client and server communicate:

1. The client starts the server as a subprocess and connects to it via stdio (standard input/output).

2. Messages are JSON-RPC over stdio: the client sends requests (e.g. "list tools", "call tool X with args Y"); the server responds with results.

3. When a tool is called, the server runs the tool logic (e.g. get_current_weather), which may call the Open-Meteo API; the server then sends the tool result back to the client over stdio.

sequenceDiagram
  participant Client as MCP Client
  participant Server as MCP Server
  participant API as Open-Meteo API

  Client->>Server: Initialize (stdio)
  Server-->>Client: Ready

  Client->>Server: ListTools
  Server-->>Client: get_current_weather, get_forecast

  Client->>Server: CallTool get_current_weather(5.98, 80.43)
  Server->>API: GET forecast?latitude=5.98&longitude=80.43
  API-->>Server: JSON weather data
  Server-->>Client: "Temperature: ...°C, Humidity: ...%..."

Summary:

Related MCP server: Weather MCP Server

Setup

Windows (PowerShell):

python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt

Linux / WSL:

# If venv fails, install first: sudo apt install python3.10-venv
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

If you cannot create a venv (e.g. WSL without python3-venv), install into your user site-packages and use python3:

pip install --user -r requirements.txt

Run the server (for Cursor / other MCP hosts)

python server.py
# or on Linux/WSL: python3 server.py

The server uses stdio transport: it reads from stdin and writes to stdout. Cursor (or another MCP host) starts this process and talks to it over those streams.

How to use the Python client

The client (client.py) is a small script that connects to the MCP server via stdio and calls the weather tools. Use it to verify that the server works without Cursor.

Steps:

1. From the project directory, with dependencies installed (see Setup):

   Windows:

   python client.py

   Linux / WSL:

   python3 client.py

2. What the client does:

   • Starts server.py as a subprocess.

   • Connects to it over stdio (stdin/stdout).

   • Sends ListTools and prints the tool names.

   • Calls get_current_weather(5.98, 80.43) (Weligama, Sri Lanka) and prints the result.

   • Calls get_forecast(5.98, 80.43, days=3) (Weligama, 3 days) and prints the result.

3. Example output (Weligama, Sri Lanka):

   Tools: ['get_current_weather', 'get_forecast']
   
   --- Weligama, Sri Lanka — current weather ---
   Temperature: 28.5°C, Humidity: 75%, Wind: 5.2 km/h, Weather code: 1
   
   --- Weligama, Sri Lanka — 3-day forecast ---
   Temperature: 28.5°C, Humidity: 75%, Wind: 5.2 km/h, Weather code: 1
   2026-02-08: max 29.1°C, min 25.2°C
   2026-02-09: max 29.5°C, min 25.8°C
   2026-02-10: max 29.2°C, min 25.5°C

You do not need to run server.py in another terminal; the client starts it automatically.

Cursor MCP config

Add the server to Cursor so the editor can call the weather tools (e.g. from chat).

Settings → MCP (or edit ~/.cursor/mcp.json):

{
  "mcpServers": {
    "weather": {
      "command": "python",
      "args": ["C:\\Users\\dolgo\\Desktop\\ai_projects\\mcp_server\\server.py"]
    }
  }
}

WSL: use python3 and the Linux path to server.py:

"command": "python3",
"args": ["/mnt/c/Users/dolgo/Desktop/ai_projects/mcp_server/server.py"]

Optional: use the venv Python so Cursor uses the same environment:

"command": "C:\\Users\\dolgo\\Desktop\\ai_projects\\mcp_server\\.venv\\Scripts\\python.exe",
"args": ["C:\\Users\\dolgo\\Desktop\\ai_projects\\mcp_server\\server.py"]

Tools

Example: “What’s the weather in Weligama?” → use latitude 5.98, longitude 80.43 (Weligama, Sri Lanka).

ML predictive model (next-day max temperature)

A small Random Forest model predicts tomorrow's max temperature from today's daily weather (max/min temp, humidity, wind, day of year). It is trained on historical data from Open-Meteo and exposed as the get_model_forecast MCP tool.

One-time setup: fetch data and train

1. Fetch historical daily weather (e.g. Weligama, 2022–today):

   python scripts/fetch_history.py
   # optional: --lat 5.98 --lon 80.43 --start 2022-01-01 --end 2025-02-06

   Output: data/weligama_history.csv.

2. Train the model:

   python scripts/train.py
   # optional: --data data/weligama_history.csv --out model/model.joblib

   Output: model/model.joblib (and MAE in °C on a held-out set).

3. Use the tool: After training, get_model_forecast(latitude, longitude) uses today's conditions (from the forecast API) and the trained model to return a predicted tomorrow max temperature.

Flow

• Training: scripts/fetch_history.py → historical daily data → scripts/train.py → model/model.joblib.

• Inference: MCP tool get_model_forecast calls the forecast API for today's daily values, builds the feature vector, runs the model, returns e.g. "Model predicts tomorrow's max temperature: 29.2°C".