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

Annotations already declare this read-only/idempotent/non-destructive, and the description adds meaningful behavior beyond that: the results are 'real in-stock' from a 'live catalog', capacities are normalized, prices are current, links go to a product page, and teen lines are filtered unless requested. This is rich, accurate behavioral disclosure.

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 compact and front-loaded, with every sentence adding value: what is searched, what inputs are expected, and what results contain. No filler or redundant elaboration.

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 read-only search tool with four optional parameters fully documented in the schema, the description is sufficient. It explains the live data source, return expectations (mL capacity, price, product links), and teen filtering. There is no output schema, but the description covers the key output characteristics an agent would need.

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 the schema already documents all four parameters well. The description restates ml_min and brand and clarifies the teen default behavior, but it does not add substantial new meaning beyond the schema descriptions.

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 uses a specific verb ('Find') with a specific resource ('real in-stock period underwear') and a clear selection criterion ('target absorbency'). It also states it draws from the live catalog and returns normalized mL capacity and current price, which clearly distinguishes it from siblings like absorbency_dataset, absorbency_translate, and find_size.

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 clear operational context: the caller provides a minimum mL capacity and optionally a brand, and teen/tween lines are excluded unless teen is true. It does not explicitly name alternatives or say when not to use this tool, but the purpose is specific enough that an agent can infer the right use case.

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