weather-learning-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_current_weatherA | Get live/current weather for a city. This tool returns real-time current conditions from the Open-Meteo weather provider (not a forecast summary and not model-invented weather). Provide a city name; optionally add state_or_region and/or country when the city name is ambiguous. The response includes the resolved location, coordinates, temperature, condition, wind, observation time, timezone, and units. |
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 1 tool
With only one tool, there is no risk of confusion between tools. The tool's purpose is clearly defined and distinct by default.
The single tool uses a clear verb_noun pattern (get_current_weather), but with only one tool, consistency cannot be meaningfully evaluated across a set.
A server named 'weather-learning-server' with only one weather tool feels incomplete for learning purposes. A 'learning' server typically benefits from multiple tools (e.g., forecast, history, alerts) to cover educational use cases.
The server only provides current weather data, missing obvious complementary tools like forecasts, historical data, or weather alerts. This severely limits its usefulness for weather-related learning or applications.