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_alertsB | Get weather alerts for a US state. |
| get_forecastC | Get weather forecast for a location. |
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 US states, while get_forecast provides forecasts for geographic coordinates. There is no overlap in functionality or ambiguity about which tool to use for each task.
Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (alerts, forecast). The naming is perfectly uniform and predictable across the tool set.
With only 2 tools, the server feels underpowered for a weather domain. While alerts and forecasts are core functions, there are obvious gaps like current conditions, historical data, or radar imagery that would be expected in a weather API. The minimal tool count limits the server's usefulness.
The tool surface is severely incomplete for a weather server. It lacks current conditions, historical weather data, radar/satellite imagery, air quality information, and marine forecasts. The two provided tools cover only narrow aspects of weather data, leaving significant gaps that will hinder agent workflows.