Global 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 |
| get_forecast_globalA | Get weather forecast for any location worldwide including India. Uses the Open-Meteo API (free, no API key required). Args: latitude: Latitude of the location (e.g. 28.6139 for New Delhi) longitude: Longitude of the location (e.g. 77.2090 for New Delhi) |
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 3 tools
get_alerts is distinct for US alerts, but get_forecast and get_forecast_global both provide forecasts; their regional scope is unclear from descriptions, causing potential confusion.
All tools follow a consistent verb_noun snake_case pattern (get_alerts, get_forecast, get_forecast_global).
With only 3 tools, the server is minimal but covers basic weather needs; it's borderline but not extreme.
Covers alerts and forecasts, but lacks current conditions, historical data, and other common weather features, leaving notable gaps.