Weather MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| get_alertsA | Get weather alerts for a US state. Args: state: Two-letter US state code (e.g. CA, NY) |
| get_forecastB | Get weather forecast for a location. Args: latitude: Latitude of the location (recommended: up to 4 decimal places) longitude: Longitude of the location (recommended: up to 4 decimal places) |
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: get_alerts retrieves weather alerts for a US state, while get_forecast provides weather forecasts for a specific geographic location. There is no overlap in functionality or ambiguity between them.
Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. This makes the tool set predictable and easy to understand.
With only two tools, this server feels under-scoped for a weather domain. While the tools cover alerts and forecasts, there are obvious missing capabilities like current conditions, historical data, or radar imagery that would be expected from a weather service.
The tool surface is severely incomplete for a weather server. It lacks fundamental operations such as getting current weather conditions, accessing radar or satellite data, or retrieving historical weather information. Agents will face significant limitations when trying to perform comprehensive weather-related tasks.