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weather_forecast-window_miami

Will it rain in Miami during your event window. Event-window weather odds for Miami: pass a start and end time (or omit them for the next six hours) and get rain probability and expected conditions across the window — built for scheduling decisions. Same shape as /v1/forecast/window with the Miami coordinates built in. [HTTP: GET /v1/forecast/miami/window · price $0.02 via x402 · docs https://x402-factory.com/docs/weather.forecast-window.miami]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoWindow end (ISO-8601), ≤12h after start. Default: start + 6h.
startNoWindow start (ISO-8601). Default: the top of the next hour.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavioral facts: it is an HTTP GET, it costs $0.02 via x402, and it links docs. The payment requirement is exactly the kind of non-obvious operational detail an agent needs. It does not mention rate limits or caching, keeping it out of 5 territory.

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

Conciseness4/5

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

Front-loaded with the core question and a compact statement of inputs and outputs. The trailing bracket of HTTP/price/docs is metadata but earns its place by conveying payment and endpoint details; overall efficient.

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?

There is no output schema, so the description must describe returns, which it does ('rain probability and expected conditions across the window'). With payment and endpoint disclosed and both params covered by the schema, an agent has enough to call it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both start and end with their defaults. The description restates the omit-to-default behavior ('next six hours') but omits the schema's key ≤12h window constraint, adding little beyond the structured fields.

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

Purpose4/5

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

States a specific verb and resource ('Will it rain in Miami during your event window', 'rain probability and expected conditions across the window'), and clarifies its relationship to the generic window tool ('Same shape as /v1/forecast/window with the Miami coordinates built in'). It does not explicitly distinguish itself from the sibling point-forecast tool weather_forecast_miami, so it falls short of a 5.

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?

'built for scheduling decisions' and 'pass a start and end time (or omit them for the next six hours)' imply the intended use, but no alternative tool is named and there are no explicit when-not conditions. Usage is inferable but not spelled out.

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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