MCP Weather
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ACCUWEATHER_API_KEY | Yes | Your AccuWeather API key (required to fetch weather data) |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| weather-get_hourlyA | Get hourly weather forecast for the next 12 hours |
| weather-get_dailyA | Get daily weather forecast for up to 15 days |
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 2 tools
The two tools have clearly distinct purposes: one provides daily forecasts for up to 15 days, while the other provides hourly forecasts for the next 12 hours. There is no overlap in functionality, and an agent can easily differentiate between them based on the time granularity and forecast duration.
Both tools follow a consistent naming pattern with a prefix 'weather-' followed by a verb_noun structure (get_daily, get_hourly). This pattern is predictable and enhances readability, making it easy for agents to understand the tool's purpose at a glance.
With only 2 tools, the server feels under-scoped for a weather domain. While the tools cover forecast retrieval, there are obvious gaps such as current weather conditions, historical data, or location-based searches, making the set too thin for comprehensive weather-related tasks.
The tool surface is severely incomplete for a weather server. It lacks essential operations like getting current weather, searching locations, or accessing historical data, which are core to weather applications. Agents will face dead ends when trying to perform basic weather queries beyond forecasts.