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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: output is given in objective millilitres, covers every brand, and includes an A/B/C data-quality grade. It also attributes the data source (PeriodFinder), which is extra 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?

The description is four sentences with no fluff. The core purpose is front-loaded, followed by input modes, examples, and a source citation. Every sentence earns its place, and the structure makes the tool easy to parse quickly.

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?

For a tool with no required parameters, no output schema, and three optional inputs, the description covers the key aspects: inputs, output format, grading, and use cases. It doesn't explicitly state behavior when both brand+tier and ml_per_day are provided, but the 'OR' wording makes exclusivity implicit, so this is a minor gap rather than a significant omission.

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%, so the baseline is 3. The description adds meaning by clarifying the OR relationship between brand+tier and ml_per_day, and it provides concrete examples ('Thinx', 'Super') that map directly to the schema properties. This moves it above baseline, though the schema already does most of the work.

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's purpose: translate absorbency across brands. It specifies the exact verb ('translate'), the resource ('period-underwear absorbency'), and the inputs/outputs with concrete examples. It distinguishes itself from generic converters by emphasizing cross-brand comparison and the A/B/C data-quality grade, so an agent can tell it apart from the sibling tools.

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 description gives explicit use cases ('Use for questions like...') and explains the two input modes (brand+tier OR ml_per_day). However, it does not mention when not to use this tool or name alternative tools like absorbency_dataset or find_products, so the guidance is clear but lacks exclusions or sibling routing.

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