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
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 |
|---|---|
| get_alertsA | Get weather alerts for a US state. Args: state: Two-letter US state code (e.g. CA, NY) |
| get_forecastC | Get weather forecast for a location. Args: latitude: Latitude of the location longitude: Longitude of the 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 completely 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 target data, making them easily distinguishable.
Both tools follow a consistent verb_noun pattern with 'get_' prefix (get_alerts, get_forecast). The naming is perfectly uniform and predictable across the tool set.
With only two tools, this server feels severely under-scoped for a weather domain. A weather server should ideally include tools for current conditions, historical data, radar, or multiple forecast types, making this minimal set inadequate for comprehensive weather interactions.
The tool surface is highly incomplete for a weather server. It lacks fundamental operations like getting current conditions, historical weather, radar imagery, or air quality data. Agents will face significant gaps when trying to perform common weather-related tasks.