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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds meaningful behavioral context beyond that: how scores are computed (three-layer cloud-cover, moon illumination, darkness window), the inclusion of a light-pollution estimate, and the uncertainty caveat ('forecasts beyond ~7 nights are increasingly uncertain'). It also instructs the agent to 'Present scores/verdicts verbatim' and notes that skies beyond 7 days are provisional, which are valuable behavioral traits.

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 description is well-structured and appropriately sized for the tool's complexity. It opens with the core purpose, follows with usage examples and calculation details, then provides a clear Args section and a compact Return structure. No sentence is wasted, and it remains readable despite its length.

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 compensates by listing the return fields ({timezone, light_pollution, nights:[...], best_window, uncertainty_note, attribution}). It also covers edge cases like uncertainty, provisional data, and the instruction to present scores verbatim. This makes the tool fully understandable in context.

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?

The schema has no descriptions (0% coverage), so the description must fully explain parameters. It does: lat as 'decimal degrees (-90..90)', lon similarly, and nights with range, default, and uncertainty note. This gives the agent all necessary semantic information beyond the raw schema.

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 clearly states the tool's function: 'Score the next nights for stargazing at a location (0-100 per night).' It uses a specific verb ('score') and identifies the resource ('stargazing conditions') and scope ('at a location'). It distinguishes itself from sibling tools by emphasizing per-night scoring that combines cloud cover, moon, and darkness window, while siblings handle site finding, aurora, or light pollution.

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?

The description provides explicit usage scenarios: 'Use for "when should I stargaze this week near X", "is Saturday any good for the Milky Way", trip planning.' This gives clear context for when to use the tool. However, it does not explicitly mention alternatives or conditions when not to use it, so it falls short of a 5.

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