kohls_category

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

With no annotations, the description carries the full burden, and it does this well: it says the response only includes page 1, enumerates the normalized product fields, and precisely explains the difference between a 404 for an unknown category and a 404 for a zero-result response. It could add rate-limit or authentication notes, but the core behavior is fully predictable.

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 with a clear, concise first sentence. The remaining sentences are dense but purposeful — encoding rules, facet follow-up usage, and error semantics are all in. There is minor redundancy in repeating 'Kohl's category or curated campaign page,' but every sentence yields real working knowledge an agent needs.

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 category browser with no output schema, the description covers the full call surface: purpose, return fields, category-string syntax, percent-encoding, follow-up discovery via facets, and error/corner-case semantics. An agent has enough to invoke it correctly and to decide whether results are useful.

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 schema description for 'category' is present but minimal (100% coverage), while the tool description adds substantial meaning: concrete examples like 'Room:Dorm' and 'Department:Kitchen & Dining', the '+ / %2B' encoding rule, how to combine multiple dimensions, and the fact that facet values can be used directly as follow-up categories. This goes far beyond the redundant schema text.

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 pair: 'Browse a Kohl's category or curated campaign page.' It then defines the concrete return value: a page-1 product grid with normalized product data and facets for further navigation. This makes it clearly distinct from sibling tools like kohls_product_reviews, kohls_stores, and kohls_suggest, even without naming them.

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 strong context for when to use this tool: to browse Kohl's category or campaign pages and to discover the taxonomy via returned facet values. It also provides practical guidance such as following facets from a known category and handling 404 vs. zero-result responses. However, it never explicitly names sibling tools or states when not to use it, stopping just short of clear exclusion guidance.

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