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weather_forecast_portland

Hourly Portland weather forecast (blend model, method labeled). Hourly weather forecast for Portland: temperature, precipitation probability, and wind, up to 168 hours out. NWS-derived series, bias-blended with our calibrated station model where covered — every response labels the method that produced it. Same shape as /v1/forecast/point with the Portland coordinates built in. [HTTP: GET /v1/forecast/portland · price dynamic ($0.01 for hours ≤ 48, $0.03 beyond) via x402 · docs https://x402-factory.com/docs/weather.forecast.portland]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursYes
unitsYesimperial

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
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 well: it discloses the NWS-derived series, the bias-blend with a calibrated station model, per-response method labeling, HTTP verb, and dynamic pricing tiers ($0.01 ≤48h, $0.03 beyond) via x402. It omits auth mechanics, rate limits, and error behavior, so it falls short of a 5.

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 capability, and the bracketed HTTP/pricing/docs block packs real value into one line. The opening two sentences overlap ('Hourly Portland weather forecast' vs 'Hourly weather forecast for Portland'), a minor redundancy that keeps it out of the top band.

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?

No output schema and no annotations, yet the description covers returned variables, horizon, provenance, pricing, endpoint, and docs link — enough for an agent to call it credibly. The remaining gap is the two undocumented parameters and the 168-vs-240 horizon discrepancy.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, and it largely does not: 'units' is never mentioned, and 'hours' is only gestured at via 'up to 168 hours out,' which conflicts with the schema's maximum of 240. This leaves the agent with a misleading horizon and an undocumented unit enum.

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 verb+resource (hourly Portland weather forecast), enumerates the returned variables (temperature, precipitation probability, wind), gives the horizon, and explicitly distinguishes itself from the generic sibling by noting it is the point forecast 'with the Portland coordinates built in.' An agent can separate it from weather_forecast-point and the window variants 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?

The 'same shape as /v1/forecast/point with Portland coordinates built in' line gives clear selection context: use this when you want Portland specifically rather than a generic point lookup. It does not, however, address weather_forecast-window_portland or state any exclusions, so it stops short of explicit when/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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