MCP Weather 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 current weather conditions for a city. Args: city: City name, e.g. "Berlin". country: Optional country filter, e.g. "DE" or "Germany". Returns: A JSON object with location info and current weather (temperature, wind speed, etc.). |
| get_daily_forecastB | Get a simple daily weather forecast for the next N days for a city. Args: city: City name, e.g. "Berlin". country: Optional country filter, e.g. "DE" or "Germany". days: Number of days to include (1–7). Returns: A JSON object with location info and an array of daily forecasts. |
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_current_weather retrieves current conditions, while get_daily_forecast provides future predictions. There is no overlap in functionality, and an agent can easily differentiate between immediate weather data and multi-day forecasts.
Both tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive names (current_weather, daily_forecast). The naming is uniform and predictable across the toolset, making it easy for agents to understand and use them.
With only 2 tools, the server feels under-scoped for a weather domain. A typical weather API would include more operations such as historical data, alerts, or hourly forecasts. This minimal set limits agent capabilities and may require workarounds for common weather-related tasks.
The toolset is severely incomplete for a weather server. It lacks essential operations like historical weather data, severe weather alerts, air quality information, and hourly forecasts. Agents will face significant gaps when trying to perform comprehensive weather analysis or respond to diverse user queries.