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RachelMantel

MCP Playwright Weather Israel

by RachelMantel
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Python
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select_weather_forecast_city_israel

Select a city from the autocomplete list using its index to load the corresponding weather forecast page for Israel.

Instructions

Click a city from the autocomplete list and load its forecast page.

Args:
    index: Which suggestion to click, numbered from 0. Defaults to 0, the
        first item - but the first item is not always the city itself, so
        prefer the index whose text matches the city the user asked for.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does disclose the key behavior: it clicks a suggestion and navigates to the forecast page. It also warns that the first suggestion may not be the city itself, which is a non-obvious behavioral caveat an agent needs to avoid incorrect selection.

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?

The purpose sentence is front-loaded and the Args block is compact, giving only the information needed to pick the right index. There is no filler or repetition.

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?

For a one-parameter UI-interaction tool with an output schema, the description is close to complete. A minor gap is that prerequisites are only implied: an agent must infer that the autocomplete list already exists, presumably from a sibling tool, and that a matching suggestion should be available.

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 coverage is 0%, but the description fully compensates for the only parameter: index is defined as a zero-based suggestion position, its default is explained, and a decision rule is provided for preferring the matching city text. This goes beyond the schema's bare integer/default definition.

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 concrete action and target ('Click a city from the autocomplete list') and the expected outcome ('load its forecast page'). This clearly distinguishes it from siblings such as enter_weather_forecast_city_israel, open_weather_forecast_israel, and extract_weather_forecast_israel.

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

Usage Guidelines3/5

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

The autocomplete-list phrasing implies the tool should be used after a city search has produced suggestions, but it does not explicitly mention alternatives or exclusions relative to sibling tools. The guidance is about choosing an index, not about when to select this tool rather than another.

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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