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 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 between them.
Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with the same verb 'get' and descriptive nouns. This makes the tool set predictable and easy to understand.
With only two tools, the server feels thin for a weather domain. It lacks basic operations like current conditions, historical data, or location search, making it incomplete for typical weather-related tasks.
The tool set is severely incomplete for a weather server. It misses core functionalities such as current weather, radar data, air quality, or unit conversions, leaving significant gaps that will hinder agent workflows.