UK Weather & Travel Outfit Recommender MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MET_OFFICE_API_KEY | Yes | Met Office DataHub API key, obtainable from https://datahub.metoffice.gov.uk/ |
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
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_outfit_recommendationA | Get clothing recommendations based on weather and travel plans. Analyses current location, destination, travel time, and weather forecast to suggest what to wear. |
| get_weather_forecastA | Get detailed hourly weather forecast for a UK location |
| get_travel_infoA | Get travel time and distance between two locations |
| set_user_preferenceA | Store a user preference like home/work addresses or other settings. Use this when the user wants to save a location for future use (e.g., 'save my home as Bristol' or 'set work to Bath'). |
| get_user_preferencesA | Retrieve all stored user preferences or get a specific preference by key. Use this to see what locations the user has saved. |
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 5 tools
Each tool has a distinct purpose: outfit recommendation, weather forecast, travel info, and preference storage. No overlap or confusion between them.
All tool names follow a consistent verb_noun pattern: get_outfit_recommendation, get_weather_forecast, get_travel_info, set_user_preference, get_user_preferences. The pattern is clear and uniform.
With 5 tools, the server is well-scoped for its purpose. Each tool contributes to the core workflow of recommending outfits based on weather and travel, plus necessary preference management.
The core functionality is covered: weather, travel, recommendation, and preference storage/retrieval. Minor gaps include no explicit update/delete for preferences, but setting a preference can overwrite, so this is not a critical dead end.