weather_forecast

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

Annotations only cover readOnlyHint and openWorldHint, and the description adds genuinely non-obvious behavior: seeing/transparency are 0-100 scores, the API ignores temp_unit for daily stats, and hourly rows deliberately omit temperature/dew point because the upstream API was non-deterministic. That last detail is exactly the kind of provenance an agent needs to avoid misreporting values.

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

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

The purpose and return fields are front-loaded, followed by parameter and caveat notes. Sentences are dense and each carries information, though the paragraph could be broken into clearer bullets for the two distinct concerns (output fields vs. hourly_nights behavior).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description usefully enumerates the returned fields and clarifies score ranges, which covers the return-value gap. It remains incomplete on inputs — nothing explains how lat/lon/timezone should be supplied or what happens when they are omitted.

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

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

Schema description coverage is 0%, so the description carries the full burden, but it only explains one of four parameters: hourly_nights (N nights of dark hours, 0 = summary only). lat, lon and timezone — essential for a location-based forecast — get no explanation of format, defaults, or whether they are required.

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

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

The description names the resource precisely ('Astronomy forecast') and enumerates the per-night data it provides (cloud cover, seeing, transparency, humidity, wind, temperature, dew risk, moon illumination), so an agent knows exactly what comes back. It does not explicitly contrast itself with siblings like sky_times or astro_events, so it falls short of the top score.

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

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

Usage is implied rather than stated: the agent can infer this is the tool for nightly astronomy weather, and the description explains the hourly_nights=0 summary mode. However, there is no explicit 'use this when...' statement or disambiguation against related siblings such as sky_times.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.