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

Goes well beyond the annotations by disclosing output behavior (confidence badge and reason per size), the out-of-range path (states it doesn't carry her size and why, e.g. bands start at 34 or cups stop at DD), and the closest_made field with an instruction to pass it on rather than guess. That is exactly the edge-case guidance an agent needs and annotations do not provide.

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?

Three sentences, front-loaded with the core definition before the usage trigger and the fallback. Dense but every clause carries information; the parenthetical example is the only slightly heavy element.

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?

No output schema exists, and the description compensates by naming the returned elements (size in brand labels, confidence, reason, closest_made) and the failure mode. For a 4-param read-only lookup, an agent has everything needed to call it and interpret the answer.

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 baseline is 3; the description adds real meaning by explaining that brand matches a shopper-written name and that results are interpreted in the brand's own lettering, which the schema's field descriptions do not convey.

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: her size at one named brand, expressed in that brand's own labels, with a worked example (32E at UK-lettered brand for a US 32DDD). This clearly separates it from bra_size_from_measurements (no brand) and translate_bra_size (label conversion only).

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?

"Use this when a shopper names a brand" gives a clear trigger, and it explicitly routes to list_bra_brands when the brand isn't found. It does not explicitly contrast with bra_size_from_measurements or translate_bra_size, but the brand condition implicitly selects this tool.

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