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weather_forecast-window_nyc

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

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 provided, the description carries the full burden and does disclose meaningful behavior: HTTP GET method, x402 price of $0.02, a docs URL, default window omission behavior, and the returned content (rain probability and expected conditions). It stops short of rate limits, auth details beyond payment, and exact error behavior, but it is substantially more informative than a bare description.

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?

The description is front-loaded: the first sentence frames the question, the second gives operational instructions, the third explains sibling shape, and the bracketed metadata is compact and useful. There is minor redundancy in repeating 'New York City' and 'event window' across the first two sentences, but the structure remains efficient overall.

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 simple two-parameter read with no output schema and no annotations, the description supplies the endpoint, price, docs link, default behavior, and a summary of the return values (rain probability and expected conditions). It could be more precise about output fields, but it is complete enough for an agent to invoke the tool 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%, and the schema already documents both parameters, their ISO-8601 format, defaults, and the ≤12h constraint. The description restates that start and end can be omitted for a six-hour default, but adds no syntax or constraint detail beyond what the schema already provides, so the baseline of 3 applies.

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 resource (New York City event-window weather odds), the action (get rain probability and expected conditions), and the scope (pass a start/end time or omit for the next six hours). It also distinguishes itself from the generic sibling by noting it is the 'same shape as /v1/forecast/window with the New York City coordinates built in,' so an agent can tell it apart without opening a schema.

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?

It gives clear context ('built for scheduling decisions') and implies when to choose this over the generic window endpoint by saying it has NYC coordinates built in. It does not explicitly state when not to use it or name every relevant sibling such as weather_forecast_nyc, but the intended use case is unambiguous.

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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