weather_Israel
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GROQ_MODEL | Yes | The Groq model identifier to use (e.g., mixtral-8x7b-32768). | |
| GROQ_API_KEY | Yes | Your Groq API key for LLM orchestration. |
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 |
|---|---|
| open_weather_forecast_israelA | Open browser and navigate to Israel weather forecast page. Returns: dict: Operation result with open and ready page for further queries |
| enter_weather_forecast_city_israelB | Enter city name in the weather forecast search field. Args: city_name: Name of the city to search for weather forecast Returns: dict: Operation result with city entered and search ready |
| select_weather_forecast_city_israelB | Select the first item from the city suggestions list. Returns: dict: Operation result with selected city |
| extract_page_context_for_llmA | Extract page content, clean it, and return context-ready text for an LLM. |
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 4 tools
Each tool maps to a discrete step in the weather forecast automation flow: open the page, enter a city, select the suggestion, and extract content. The descriptions clearly separate entering from selecting, so an agent should have little trouble picking the right tool.
The first three tools follow a similar verb_weather_forecast_city_israel pattern, but extract_page_context_for_llm breaks the convention entirely. The naming is still readable overall, but the mix of domain-specific and generic names prevents it from being highly consistent.
Four tools is well-scoped for a focused browser automation workflow: open, enter, select, and extract. Each tool has a distinct role and none feels redundant or missing.
The set covers the core weather forecast workflow end-to-end, from opening the page to returning LLM-ready content. There are minor assumptions, such as always selecting the first suggestion, but the flow is complete enough for its stated purpose.