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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description usefully adds that the ranking comes from a periodically refreshed dataset ("mis à jour régulièrement"), which explains the non-live, non-open-world behavior. It also discloses what the response contains conceptually — citation share per business and the sites ChatGPT consults. It stops short of stating freshness guarantees, coverage limits, or whether a missing city returns empty versus error.

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?

One dense sentence, front-loaded with what is returned, followed by the data source and the returned metrics. No filler or repetition; the parenthetical about the source is well-placed rather than buried.

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 only two required parameters, no nested objects and no output schema, the description covers what is needed: what comes back, that it is a ranking, and the metrics included. Gaps are secondary — list size, pagination, and empty/unknown-city behavior are unspecified, but an agent can still call it correctly.

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 100%, so both parameters (ville, métier) are already documented with examples, and the baseline of 3 applies. The description adds only the constraint that the city must be French and reinforces the métier framing, without normalizing expected input format or listing supported values.

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 gives a specific verb+object ("Renvoie les commerces que ChatGPT recommande le plus") scoped to a métier in a French city, and even names its data source (classement Apparais). It is clearly understandable without opening the schema, but it never differentiates itself from siblings such as liste_classements or checklist_visibilite_locale, which an agent would need to pick between.

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?

There is no when-to-use or when-not-to-use guidance, and no sibling is named as an alternative. The only implicit signal is the métier/ville scoping, which tells the agent what inputs are needed, not when this tool is the right choice over liste_classements or tester_mon_commerce.

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