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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds real context beyond that: personal details are withheld server-side behind an opaque token, which is a meaningful privacy disclosure for an advice tool. It stops short of describing latency, freshness, or how 'à surveiller' is determined.

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?

Two sentences, front-loaded with the verb and the shape of the answer, with the privacy guarantee placed last where it reads as a reassurance. The parenthetical item list is a bit terse and slightly cryptic, but nothing is truly redundant.

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 a full input schema, an output schema, and complete annotations, the description only needs to add orientation and any non-structural caveats — which it does via the opaque-token privacy note. A brief note on how it differs from recommend_look would close the remaining gap.

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 100% and every parameter (question, limit, detail_level, include_products, include_conversion) is documented in the schema itself. The description only faintly echoes behavior via the mention of 'sources', which maps loosely to detail_level=full, but adds no syntax, defaults, or constraints of its own — baseline 3.

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?

Names a specific action ('je diagnostique une question de style') and enumerates what the response covers (look nommé, capsule, à surveiller, sources), so the agent knows this is an open style-advice query tool. It does not, however, distinguish itself from siblings like recommend_look or recommend_weather_look that plausibly compete for the same style questions.

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 only implied through the phrasing of a natural-language style question and the capsule vocabulary (pièce, ancre, chaussures, sac, bijou). There is no explicit when-to-use, when-not, or pointer to recommend_look / shop_the_look as alternatives, which matters given several overlapping style siblings.

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