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meanochi

Weather MCP - Israel Edition

by meanochi

get_weather_forecast_content_israel

Extracts and cleans the forecast text from the currently open browser page, giving the LLM the information needed to answer weather questions for Israeli cities in conversation. Use after selecting a city forecast.

Instructions

שולפת ומנקה את תוכן הטקסט של דף התחזית הפתוח כרגע בדפדפן, כדי לספק ל-LLM את המידע הדרוש לענות על שאלות לגבי התחזית ישירות בשיחה (RAG).

יש להריץ לאחר select_weather_forecast_city_israel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description carries the full burden. It discloses that the tool reads and cleans text content without modifying the page, but it does not describe failure behavior (e.g., what happens if no forecast page is open) or any side effects. A 3 reflects adequate but not rich behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences in Hebrew, front-loaded with the action and purpose, followed by the prerequisite. Every sentence earns its place with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and zero parameters, the description provides the essential context: what the tool does, its RAG purpose, and its required predecessor. It could add more detail on the 'cleaning' step or error conditions, but nothing critical is missing for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so parameter semantics are trivially satisfied. The description appropriately focuses on behavior rather than parameters, matching the baseline 4 for tools with no parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('retrieves and cleans the text content of the currently open forecast page in the browser') with a clear resource and scope. It is easily distinguished from sibling tools like open_weather_forecast_city_israel and select_weather_forecast_city_israel because it explicitly targets the already-open page and mentions its RAG purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'יש להריץ לאחר select_weather_forecast_city_israel' (must run after select_weather_forecast_city_israel), giving clear sequential context. It does not list when-not-to-use cases, but the prerequisite effectively routes the agent away from using it prematurely.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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