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

Annotations already cover the safety profile (read-only, idempotent, not open-world), so the description earns credit for adding non-obvious behavior: matching is done via a reviewed alias table plus whole-word text match, and each fiche reports which method applied. It does not mention result caps or ordering.

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?

Front-loaded with the resource and scope, then the optional-parameter rule, then matching semantics — a logical order with no filler. The parenthetical date/count clause is dense but earns its place as provenance.

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 (fiche number, date, risks, motif in French, commercialisation periods, fiche link) and notes the French-language content. Minor gaps remain around result volume/ordering, but an agent can call this confidently.

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 baseline is 3; the description goes beyond it by explaining the semantic consequence of omitting model and by disclosing the alias/whole-word matching strategy that governs how brand and model strings are interpreted.

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?

States a specific verb+resource (recall notices for a brand/model) with the exact data source (RappelConso, DGCCRF), coverage window, and the concrete fields returned. The name/title pair 'recalls_for_model' vs. sibling 'fr_recalls_search' is clearly differentiated by the 'for a brand and model' scoping.

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?

Gives a clear operational rule — 'Omit model to list a brand' — which is the main usage fork for this tool. It does not, however, tell the agent when to prefer this over fr_recalls_search or eu_safety_gate_recalls, so the routing decision against siblings is left partly to inference.

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