gucci_recommendations

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

With no annotations provided, the description carries the burden. It discloses the read-only nature implicitly (returns recommendations), specifies the output format (normalized summaries with name, price, image, stock), and explains the two data sources. It does not mention failure modes or rate limits, but for a recommendation tool this is adequate 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 concise, using three sentences to convey purpose, the two modes, and the source of the style_code. It front-loads the core function and is free of fluff, every sentence adds necessary information.

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 simple tool with two optional parameters and no output schema, the description is nearly complete. It specifies the return content (product summaries with specific fields), clarifies the two modes, and hints at the data source. It lacks explicit details on return structure (e.g., array vs. object) or edge cases, but these are minor given the tool's simplicity.

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?

The input schema already thoroughly describes both parameters (max: default 10, cap 20; style_code: product code or omit for trending). The description adds extra value by noting that style_code originates from sibling tools, which helps agents source it correctly. Since schema coverage is 100%, this extra context elevates it above 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 clearly states it returns Gucci product recommendations, specifically a 'recommendations shelf' with normalized product summaries. It distinguishes two modes (based on style_code presence) and even mentions the source of style_code from sibling tools, making the purpose precise and differentiating it from other tools.

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 explains the two usage modes (with style_code for product-specific carousel vs. omitting for general trending shelf) and explicitly states that style_code comes from gucci-search or gucci-category. This provides practical guidance on how to invoke the tool, though it does not explicitly compare with alternative recommendation tools from other brands. Still, it gives clear contextual direction.

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