gucci_product

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

No annotations are provided, so the description carries the full behavioral burden. It does add genuine context beyond a bare getter by disclosing the data provenance ('combining Gucci's own structured product record with the product page's own marketing copy') and the normalization aspect. Yet it leaves out error behavior, availability edge cases, and whether stock/price are live-refreshed, which would matter for a tool with zero annotation coverage.

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 a single well-organized paragraph that front-loads the core action and return scope before the provenance note. The field list is dense but purposeful, and the final sentence on style_code is mildly redundant with the schema. Every sentence earns its place, though the redundancy prevents a 5.

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 single-parameter, no-output-schema product detail tool, the description is largely complete: it states the input provenance, lists the returned fields extensively, and explains the data-merging behavior. Gaps are minor — no error/edge-case behavior and no statement about stock freshness — but nothing an agent needs to invoke it correctly with a valid style_code 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 description coverage is 100%, so the baseline is 3. The schema already documents style_code as coming from gucci-search or gucci-category, and the description essentially restates that same provenance rather than adding new format, pattern, or example detail. It adds no meaning beyond what the schema provides, so it stays at baseline.

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 opens with a specific verb+resource ('Get a Gucci product') and immediately distinguishes itself by scope: 'full normalized product detail for one style.' It enumerates the returned fields concretely (name, marketing description, breadcrumb trail, brand line, gender, colors, materials, sizes, price, images, stock) and notes the combination of structured product data with marketing copy. An agent can clearly tell this product-detail tool apart from sibling gucci_search, gucci_category, and gucci_recommendations.

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 context for when to call the tool: it consumes a style_code produced by gucci-search or gucci-category, implying the workflow of search/category first, then product detail. However, it does not explicitly name alternatives or state when-not-to-use (e.g., gucci_recommendations or gucci_size_guide), so it stops short of full exclusion guidance.

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