lululemon_category

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

With no annotations, the description carries the full burden of behavioral disclosure and does so thoroughly. It reveals the data source (lululemon's app-backend category data), the return content (normalized products with pricing, sale detection, sizes, colors, style numbers), and importantly documents edge-case behavior: pages beyond the real last page return an empty products array, and unrecognized category/cdp_hash pairs return 404.

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 three-sentence description is dense and mostly non-redundant, with the first sentence serving as an effective opening statement. There is slight repetition between 'Browse a... listing' and 'Returns one... listing page,' but overall each sentence earns its place and important details are surfaced without burying them.

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 tool with no output schema, the description covers request prerequisites, URL construction, parameter sourcing, response content, pagination semantics, and error behavior. It is complete enough for an agent to select, parameterize, and invoke the tool correctly in realistic contexts, including how to discover valid category identifiers.

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 baseline is 3, and the description adds meaningful context beyond the schema. It explains that category and cdp_hash are the two path segments of a lululemon URL, provides a concrete example, and tells the agent they can come from lululemon-categories' fields. This materially helps parameter construction; page and page_size are already fully described by the schema.

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 the resource ('a lululemon category's product listing') and the specific verb ('Browse/Returns'), making the tool's purpose unmistakable. It distinguishes itself from sibling tools like lululemon_categories by emphasizing a single category's listing page with normalized product details, and from lululemon_product by targeting category-level listing rather than individual product detail.

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 invoking the tool: it requires a lululemon category URL's path segments and explicitly points to lululemon_categories as the source for obtaining valid category/cdp_hash values. While it doesn't explicitly enumerate when not to use it versus direct product-detail tools, the listing-vs-detail distinction is strongly implied.

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