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

With readOnlyHint and openWorldHint=false already declared, the description adds real context: it reads a 46,000-watch catalogue, names fuzzy name matching, and discloses that lug-to-lug is estimated at 1.2× case when absent and flagged as estimated in the answer. That estimation caveat is exactly the kind of hidden behaviour an agent should know. It stops short of saying what happens when a name matches nothing in the catalogue.

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?

Front-loaded with what the tool computes and what it returns, then the matching and precision caveats. The middle clause sweeping three verdicts into one sentence is dense but each phrase carries distinct information. No filler, though the single long sentence is harder to scan than a short list would be.

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?

There is no output schema, so the description correctly takes on the return values — verdicts in words, a scale-drawing link, and a card — which is the critical piece. Combined with the estimation rule and name-matching note, an agent can call this correctly. The remaining gap is failure behaviour for unmatched watch names.

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 50% at top level but the nested watch properties are well documented; the description reinforces it by explaining that case_mm disambiguates between sizes of the same model and that lug_to_lug_mm is the one value the catalogue lacks. It does not mention note or thickness_mm, and adds no semantics the schema is missing. Slightly above baseline because it explains the *consequence* of supplying the numbers, not just their type.

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 and resource: fitting one to three watches to a wrist of a given size, checked against a 46,000-watch catalogue, and enumerates the outputs (lug clearance, relative wear, cuff behaviour). An agent can tell this is a fit-calculation tool rather than a general search. However, it never names the siblings (start_try_on, get_try_on_result, suggest_sizes), so the boundary between 'fit math' and 'generate a try-on image' is left to inference.

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?

Usage is only implied: you call it when someone asks how a watch wears. The one piece of genuine guidance is about optional inputs — supplying case_mm and lug_to_lug_mm 'makes the answer exact' — but that is parameter advice, not tool-selection advice. No when-not-to-use, no mention of when to prefer start_try_on or get_try_on_result instead.

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