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ChayaEtrog

MCP Playwright Weather Israel

by ChayaEtrog
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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extract_weather_forecast_israel

Get a clean markdown forecast from the currently open weather page, including current conditions and three days of hourly data for Israeli cities.

Instructions

Read the forecast off the page that is currently open, as clean markdown.

Call this after select_weather_forecast_city_israel, and call it with NO
arguments. The defaults already return the current conditions (temperature,
wind, gust, wind direction, humidity, sunrise, sunset, last update) plus three
days of hourly forecast, which is enough for almost any question. Shrinking
the window costs you the data you are about to be asked about.

Args:
    max_days: Days of hourly forecast. Leave unset unless the user explicitly
        asks about several days ahead. Clamped to 1-10.
    max_hours_per_day: Hours per day. Leave unset. Clamped to 3-24.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_daysNo
max_hours_per_dayNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the output format, default content (current conditions plus 3-day hourly forecast), clamping ranges, and the trade-off of reducing the window. It omits error cases (e.g., page not open) but covers essential behavioral traits.

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?

Well-structured and front-loaded: first sentence states purpose, second gives call sequence, third explains defaults, then detailed args. No wasted words; every sentence serves a distinct purpose.

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

Completeness5/5

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

Given the output schema exists and annotations are absent, the description covers all essentials: dependency on prior step, default sufficiency, and parameter constraints. An agent can correctly call it with confidence.

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

Parameters5/5

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

Schema has zero descriptions (0% coverage), so the description must compensate. It explains each parameter's purpose, default behavior, clamping range, and when to leave unset, adding substantial meaning beyond the raw schema.

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 clearly states a specific action: reading the forecast from the currently open page and returning it as clean markdown. It differentiates from siblings (open, select, close) by focusing on extraction after selection, so an agent can tell them apart without checking schemas.

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

Usage Guidelines5/5

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

Explicitly instructs to call after select_weather_forecast_city_israel, advises using no arguments, and explains when to adjust parameters (only for multi-day questions). It also warns against shrinking the window, providing clear when-to-use and when-not-to-use guidance.

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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