MCP Weather Aggregator
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENAI_API_KEY | No | API key for OpenAI (required for AI deduction) | |
| WEATHERAPI_KEY | No | API key for WeatherAPI (optional, enables additional weather source) | |
| VISUALCROSSING_KEY | No | API key for Visual Crossing (optional, enables additional weather source) | |
| OPENWEATHERMAP_API_KEY | No | API key for OpenWeatherMap (optional, enables additional weather source) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_locationA | Search for a location by name to get coordinates. Args: query: City or location name to search for (e.g., "Prague", "New York") Returns: JSON list of matching locations with name, country, latitude, longitude |
| get_current_weatherA | Get current weather conditions with AI-powered summary. Args: location_name: Name of the city/location (e.g., "Prague", "London") language: Language for AI summary (en, cs) Returns: JSON with current weather, AI summary, and ambient theme |
| get_weather_forecastA | Get weather forecast with AI-powered analysis. Args: location_name: Name of the city/location (e.g., "Prague", "London") days: Number of forecast days (1-16, default 7) language: Language for AI summary (en, cs) Returns: JSON with forecast, AI analysis, and ambient theme |
| get_weather_by_coordinatesA | Get weather forecast using exact coordinates with AI analysis. Args:
latitude: Latitude of the location
longitude: Longitude of the location Returns: JSON with complete weather data, AI analysis, and ambient theme |
| get_ambient_themeA | Get the ambient theme (colors/mood) for current weather conditions. Args: location_name: Name of the city/location Returns: JSON with theme name, gradient colors, and special effects |
| get_aurora_forecastA | Get Aurora Borealis (Northern Lights) forecast and visibility probability. Args: location_name: Name of the city/location (e.g. "Tromso", "Prague") Returns: JSON with current Kp index, visibility probability, and 3-day forecast |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.