Wardrobe Connect

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

Annotations only cover the safety profile (readOnly, idempotent, non-destructive), and the description adds substantial operational context: source (eBay Clothing/Shoes/Accessories), ranking order (best-match then price), dedup of repeat listings, exclusion of adult/fetish/costume and price-outlier 'bait' listings, and the extra search links returned. This is far beyond what the annotations disclose.

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?

Purpose and usage are front-loaded, but the trailing block is a long, comma-heavy enumeration of filtering and ranking rules. Every clause carries information, yet it could be broken into shorter sentences for easier scanning.

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 no output schema, the description carries the return-value burden and does: listings, ordering, and the auxiliary shop/Facebook search links, plus the filtering rules. An agent has everything needed to call it and interpret the result.

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 structured baseline already documents all five parameters; the description nonetheless adds real semantics, notably that a price or size mentioned in the query words is honoured when max_price or size is omitted, which is cross-parameter behaviour the schema does not express.

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 ('Find') and resource ('clothes') scoped to a natural-language description rather than an image, which implicitly separates it from the only sibling, find_clothes_by_image. The detail about matching colour, fabric and garment kind makes the scope of the search unambiguous.

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?

Explicitly says 'Use when the user describes a piece of clothing they want' and gives worked examples of natural requests, plus how occasion, price and size words in the query are handled. It never names the sibling tool as the alternative for image input, so the when-not is left implicit.

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

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

Beyond the readOnly/idempotent annotations, the description discloses real behavioral traits: results come only from one eBay category, are ordered by eBay's look-alike ranking, and that a non-single-item image yields an empty `matches` plus a clarifying 'say line'. The q parameter's effect on matching is also surfaced as a recommendation, which is genuine added context.

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 front-loaded: purpose first, then trigger, then input rule, then return semantics and the empty-result edge case. It is somewhat dense in the final sentence, but every clause carries information an agent would otherwise have to guess.

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 carries the return-behavior burden and does so: source corpus, category restriction, ordering, and the empty-`matches` case. With all six parameters documented in the schema, no call-critical information is missing.

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 is 3, but the description adds meaning beyond the schema: it explains that q is 'strongly recommended' because results must then match it and it 'removes look-alikes of the wrong kind', and it stresses that image_url must be a direct https link. That gives an agent priority and intent information the field descriptions alone do not.

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 definition states a concrete verb and resource ('Find clothes that look like a picture') and narrows scope to 'visually similar eBay listings from the Clothing, Shoes and Accessories category only'. The image-driven mechanism implicitly separates it from the sibling find_clothes, though the sibling is never named, so the contrast 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?

It gives an explicit trigger with concrete examples ('a Pinterest pin, a saved post, a photo') and states the failure condition when the image isn't a single item. It stops short of saying when to prefer the sibling find_clothes instead, so routing between the two is not fully resolved.

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