MCP Weather Sample
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GOOGLE_API_KEY | No | API key for Google Gemini AI model | |
| OPENAI_API_KEY | No | API key for OpenAI GPT models | |
| ANTHROPIC_API_KEY | No | API key for Anthropic Claude AI model |
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_alertsB | |
| get_forecastC | |
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 U.S. state, while get_forecast provides forecast data for a specific latitude/longitude location. There is no overlap or ambiguity between these functions.
Both tools follow a consistent verb_noun naming pattern (get_alerts, get_forecast) with identical verb usage and snake_case formatting. The naming is perfectly predictable and readable.
With only 2 tools for a weather server, the surface feels too thin for the domain. A weather service typically needs more operations like current conditions, historical data, or multi-day forecasts to be useful for agents. The count is borderline insufficient.
The tool set is severely incomplete for a weather domain. It lacks core operations like getting current conditions, historical weather data, or multi-location forecasts. Agents will hit dead ends trying to perform basic weather-related tasks with only alerts and single-location forecasts.