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weather_forecast-window_san-jose

Will it rain in San Jose during your event window. Event-window weather odds for San Jose: 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 San Jose coordinates built in. [HTTP: GET /v1/forecast/san-jose/window · price $0.02 via x402 · docs https://x402-factory.com/docs/weather.forecast-window.san-jose]

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

A4.1/5.0
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 meaningful operational facts: default behavior when times are omitted (next six hours), that San Jose coordinates are fixed, what is returned (rain probability and expected conditions), and the payment model ($0.02 via x402) plus HTTP endpoint. It omits read-only framing and rate/error behavior, but covers the essentials for a low-risk read tool.

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-loads the user-facing question and the window mechanics, then tucks endpoint/price/docs into a bracketed footer. Slight redundancy between 'your event window' and 'Event-window weather odds' costs it the top mark.

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 two-optional-parameter read tool with no output schema and no annotations, the description is largely sufficient: it names the location, the window semantics, the defaults, and the returned fields. It could still state read-only status and the maximum window length, minor gaps given the tool's low complexity.

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 both start and end are already documented with format and defaults. The description restates the 'omit for next six hours' default, which is consistent with the schema but adds no new syntax or constraint detail (e.g., the 12h max lives only in the schema). Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific question the tool answers ('Will it rain in San Jose during your event window'), the resource (event-window weather odds), and the location baked in. It distinguishes itself from weather_forecast_san-jose (point) and the generic weather_forecast-window by noting the San Jose coordinates are prebuilt.

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?

Explains how to scope the window ('pass a start and end time (or omit them for the next six hours)') and frames the intent ('built for scheduling decisions'). It references 'the same shape as /v1/forecast/window' as the alternative, implicitly routing San Jose requests here, but gives no explicit when-not 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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