MCP Weather Aggregator
# MCP Weather Aggregator
AI-powered weather aggregation from **6 sources** with intelligent deduction using GPT-5-mini.
Works as both a **REST API** for web apps and an **MCP Server** for AI assistants (Claude, Cursor, etc.).
## โจ Features
- ๐ค๏ธ **Multi-source aggregation** - Open-Meteo, OpenWeatherMap, WeatherAPI, Visual Crossing, MET Norway (Yr.no), DWD (Bright Sky)
- ๐ค **AI-powered deduction** - GPT-5-mini analyzes differences and deduces most accurate values
- ๐ **Dual Mode** - Runs as REST API (FastAPI) or MCP Server (FastMCP)
- ๐จ **Ambient theming** - Dynamic gradients based on weather/time (sunny, rainy, storm, night, **fog, sandstorm, blizzard, aurora**...)
- ๐ง **Advanced Data Processing** - Uses **EWMA** (Exponential Smoothing) for forecast curves and **Kalman Filter** for sensor fusion
- ๐ **Confidence scores** - Based on source agreement (0-1)
- ๐
**Forecasts** - Daily (up to 16 days) + Hourly (24 hours)
- ๐
**Astronomy** - Sunrise/sunset, moon phases
- ๐ **Multi-language** - EN, CZ
- ๐ **Geolocation** - Automatic location detection with reverse geocoding (shows city name, not coordinates)
- ๐ **Smart Caching** - Redis-backed caching for geocoding (24h), weather data (30m), and aurora (1h)
- ๐ก๏ธ **Security** - Rate limiting, security headers, and input sanitization
- ๐ **Aurora forecast** - Real-time aurora borealis visibility prediction from NOAA data
## ๐ Quick Start
### 1. Configure API Keys
```bash
cp .env.example .env
```
Edit `.env`:
```env
# Required for AI deduction
OPENAI_API_KEY=sk-...
# Weather providers (add keys to enable more sources)
OPENWEATHERMAP_API_KEY=your_key # openweathermap.org
WEATHERAPI_KEY=your_key # weatherapi.com
VISUALCROSSING_KEY=your_key # visualcrossing.com
```
### 2. Install Dependencies
This project uses `uv` for dependency management.
```bash
# Windows
curl -LsSf https://astral.sh/uv/install.ps1 | powershell -c -
# Install project dependencies
uv sync
```
### 3. Run Application
#### Option A: Run as MCP Server (for AI Assistants)
Connect this server to your MCP client (Cursor, Claude Desktop, etc.).
```bash
# Run directly
uv run mcp-weather
# OR via python module
uv run python -m src.server
#### Option C: Run with Docker (Recommended)
Full stack (Backend + Frontend + Redis) in one command:
```bash
docker-compose up --build
```
- Frontend: http://localhost:3000
- API: http://localhost:8000
- Redis: localhost:6379
```
### 5. Configure Claude Desktop
To use this server with Claude Desktop, edit your config file:
- **Windows**: `C:\Users\USERNAME\AppData\Roaming\Claude\claude_desktop_config.json`
- **Mac/Linux**: `~/Library/Application Support/Claude/claude_desktop_config.json`
Add the following configuration (adjust path to your project):
```json
{
"mcpServers": {
"weather": {
"command": "C:\\Path\\To\\mcp-weather\\.venv\\Scripts\\python.exe",
"args": [
"-m",
"src.server"
],
"cwd": "C:\\Path\\To\\mcp-weather",
"env": {
"PYTHONPATH": "C:\\Path\\To\\mcp-weather"
}
}
}
}
```
> **Note:** The `PYTHONPATH` environment variable is crucial for the server to find the `src` module correctly.
**Available MCP Tools:**
- `search_location(query)` - Find coordinates for a city
- `get_current_weather(location_name)` - Get current weather + AI summary
- `get_weather_forecast(location_name, days)` - Full forecast + AI deduction
- `get_weather_by_coordinates(lat, lon)` - Weather for exact location
- `get_ambient_theme(location_name)` - Get UI theme colors for current weather
- `get_aurora_forecast(location_name)` - Aurora Borealis forecast & visibility
#### Option B: Run as REST API (for Frontend)
Starts the FastAPI server on `http://localhost:8000`.
```bash
# Run directly
uv run mcp-weather-api
# OR via python module
uv run python -m src.api
```
### 4. Run Frontend
```bash
cd frontend
npm install
npm run dev
```
Open **http://localhost:3000** ๐
## ๐ก API Endpoints (REST Mode)
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/search` | POST | Search locations by name |
| `/weather/current` | POST | Current weather + AI summary |
| `/weather/forecast` | POST | Full forecast + AI analysis |
| `/weather/coordinates` | POST | Weather by lat/lon (auto-resolves city name) |
| `/aurora` | POST | Aurora borealis forecast from NOAA |
| `/theme` | POST | Get ambient theme colors |
### Example Request
```bash
curl -X POST http://localhost:8000/weather/forecast \
-H "Content-Type: application/json" \
-d '{"location_name": "Prague"}'
```
### Response includes:
- `current` - Aggregated current weather
- `daily_forecast` - 7-day forecast
- `hourly_forecast` - 24-hour forecast
- `ai_summary` - AI reasoning about the weather
- `confidence` - 0-1 score based on source agreement
- `sources` - List of providers used
- `ambient_theme` - Theme name + gradient colors
## ๐๏ธ Tech Stack
| Component | Technology |
|-----------|------------|
| Backend | Python 3.14+, `uv` |
| API Framework | FastAPI (REST) |
| MCP Framework | FastMCP (MCP Server) |
| Frontend | Next.js 16.1.4, Tailwind v4, shadcn/ui |
| AI | OpenAI GPT-5-mini |
| Weather | Open-Meteo (free), OpenWeatherMap, WeatherAPI, Visual Crossing |
## ๐ Project Structure
```
mcp-weather/
โโโ src/
โ โโโ api.py # FastAPI REST server
โ โโโ server.py # MCP Server (FastMCP)
โ โโโ aggregator.py # AI weather aggregation logic
โ โโโ models.py # Pydantic data models
โ โโโ providers/ # Weather API providers
โโโ frontend/ # Next.js app
โ โโโ src/
โ โโโ app/
โ โโโ components/weather/
โ โโโ lib/
โโโ .env # API keys
โโโ pyproject.toml # Python dependencies
โโโ uv.lock # Lock file
```
## ๐ฎ Future Plans
- **NOAA Aviation Weather** - METARs, TAFs, aviation advisories
- **NOAA Marine Weather** - Ocean/coastal forecasts
- **NOAA Solar/Space** - Enhanced UV index and solar radiation data
## License
Copyright (c) 2026 Tomรกลก Stark
All rights reserved.
This code is provided for viewing purposes only.
You may not copy, modify, distribute, or use this code,
in whole or in part, without explicit written permission
from the author.
TDQS
Scored across 6 tools
Most tools target distinct resources, but get_weather_forecast and get_weather_by_coordinates are essentially the same forecast operation differentiated only by input, and get_ambient_theme duplicates a field already returned by the weather tools. Descriptions help clarify the intended usage, but the boundaries are not fully clean.
All tools use snake_case verb-first names, predominantly get_* plus search_location. The naming pattern is consistent and predictable across the entire set.
Six tools is an appropriate size for a weather aggregator, covering location lookup, current weather, forecasts, coordinate-based retrieval, ambient themes, and aurora forecasts. Each tool has a recognizable place in the overall workflow.
The core weather workflow is covered well: search locations, get current conditions, get forecasts, and use coordinates. Minor gaps like historical weather, weather alerts, or a dedicated current-weather-by-coordinates endpoint can be worked around but are not fatal.