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

Annotations already establish the safe read-only, idempotent, non-destructive profile, and the description adds substantive value by enumerating what comes back (overall and per-profile scores, per-criterion scores, best new and refurbished offers). It does not cover behavior for unknown models, defaulted parameters, or result volume, so it stops short of full transparency.

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?

A single front-loaded sentence that lists the returned data and the lookup key with zero filler; every clause carries information.

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 summarizes the returned content, so an agent knows what it will get for a 4-parameter read-only lookup. Minor gaps remain around behavior on unmatched model names and the effect of the pays/langue defaults.

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 coverage is already 100%, so the baseline is 3; the description adds meaning with concrete model-name examples ('Galaxy S25', 'iPhone 16 Pro') that clarify the expected query format beyond the schema's 'Model name, brand optional'.

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 (scores overall and per buyer profile, per-criterion scores, best new and refurbished offers) and scopes it to a single model looked up by name, with concrete examples. It implicitly separates itself from comparer_modeles by stressing 'one model', though no sibling is named explicitly.

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 phrase 'for one model found by name' implies this is the single-model lookup path, distinct from the comparison and best-picks siblings, but there is no explicit when-to-use / when-not guidance and no named alternative.

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