jcrew_category

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the page size cap (up to 60 products), the result_count behavior (reports true total regardless of page size), and the edge case of hub-level categories returning an empty result rather than an error. It does not mention auth requirements or rate limits, but for a read-only browse these are less critical; the disclosed behaviors are substantial and honest.

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 compact: three sentences that cover purpose, parameter specifics, and an edge case. Every sentence earns its place with actionable information. The main purpose is front-loaded, and the parameter explanations are clear and efficient.

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 responsibility of explaining what the tool returns. It states it returns one page (up to 60 products) and mentions result_count, which gives a sense of the response. It also explains the empty-result behavior. While it does not enumerate product fields, that is likely available via jcrew_product, and the core behavior is adequately covered for an agent to call and interpret the tool.

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 description adds meaningful extra context: the category parameter is defined as the slash-separated path segment after the storefront's /plp/ with a concrete example, the site parameter's default is stated, and the page parameter's SFCC-native nature and the result_count behavior are explained. These go beyond the schema's basic descriptions.

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 and resource: 'Browse a J.Crew or J.Crew Factory category.' It clarifies the tool returns a page of a category listing, distinguishing it from sibling tools like jcrew_categories (which discovers valid category paths) and jcrew_product (which fetches a single product). The category path format is explained with an example, leaving no ambiguity about what this tool does.

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 explicitly instructs users to 'use jcrew-categories to discover every valid value instead of guessing from storefront URLs.' This is direct when-to-use guidance that routes agents to the correct discovery tool for input generation. It also explains the difference between hub-level and leaf categories, which informs whether results will be empty.

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