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

With no annotations, the description carries the full burden. It discloses that it includes rapid cooling, reheating rules, and recommendations for sensitive populations. It also notes the optional filter behavior. However, it does not describe the return format or any potential limitations (e.g., pagination), but the provided detail is substantial.

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 bilingual and compact, with each sentence adding new information. It front-loads the core purpose and includes examples, while explicitly noting the distinction from a sibling tool. No redundancy or filler.

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?

Given no output schema, the description manages to convey the full scope of results: mandatory temperatures, cooling, reheating, and vulnerable populations. It is complete enough for a lookup-style tool with one optional parameter, though it could mention the return format or data structure.

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 already documents the single parameter with 100% coverage. The description adds value by explaining the search behavior (keyword match in identifier or label) and giving concrete examples ('volaille', 'boeuf', 'poisson'). It also clarifies the default behavior when the parameter is absent.

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 returns mandatory core cooking temperatures by food type in French catering, with specific examples (volaille 74°C, bœuf haché 70°C). It also distinguishes itself from the sibling get_haccp_temperatures, making its purpose unambiguous.

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?

It explicitly states this tool is distinct from 'températures de conservation' available via get_haccp_temperatures, providing a clear alternative. It also describes the optional 'type_aliment' filter behavior, telling users they can filter by food type or process.

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