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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds useful scope context by stating the data source ('the tool reads within's own catalog') and a documentation link, plus the fact that the response is per-size rows. It does not address behavior on missing items, missing usual_size, or unavailable size charts, which is the remaining gap.

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 content list is front-loaded and the usage sentence follows logically. The opening sentence is a long comma-separated run that takes effort to parse, and the url-format detail partly duplicates the schema's own url description. No sentence is wholly wasted, but tightening the first sentence would help.

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 a rich output schema and full annotation coverage, the description only needs to carry selection and invocation context, and it does: what the answer holds, when to reach for it, and how to identify the piece. The one omission is what happens when the piece or the size chart can't be found, which matters for an agent deciding whether to call it or fall back.

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 would be 3, but the description adds real meaning: it explains that a piece can be identified either by url or by the boutique_domain + id pair from a previous result, which clarifies why both paths exist. It also explains the purpose of usual_size ('in any system, so the answer points at the rows that matter') rather than just restating it.

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 names the resource and enumerates exactly what the answer contains: the piece's own measurements, the designer's body chart, fit notes, model size/height, and a row per in-stock size. That is far more specific than a restatement of the name. It stops short of naming a sibling (e.g. convert_size) as the alternative for pure unit conversion, so differentiation is inferred rather than stated.

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 gives a clear triggering context: 'Use this when someone asks what size to take, whether an item runs big or small, or whether to size up or down.' That maps real user questions onto the tool. There is no explicit when-not guidance or named alternative for adjacent questions like size conversion or cross-item comparison.

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