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

Beyond the read-only and idempotent annotations, the description discloses the return shape, the up-to-20 result cap, price units, availability, image and URL fields, color/size matching semantics, and typo/dropped-word signaling. This is substantial behavioral context for a tool with no output schema.

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 purpose and return behavior, then adds only the non-obvious color/size and signal semantics. It is appropriately sized for an eight-parameter search tool with no output schema.

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?

Given the lack of an output schema, the description supplies enough return-value context for an agent to interpret results correctly. It also makes the follow-up workflow clear by naming get_product.

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 already 100%, so the schema carries most parameter meaning. The description adds useful interpretation for query/color/size interactions, especially what matchedColors means and what an empty match implies.

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 states a specific verb and resource: searching blank apparel by keywords and filters. It also distinguishes the follow-up path by telling the agent to use the returned slug with get_product.

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?

It gives clear search context and points to get_product for the next step, but it does not explicitly compare against siblings like get_inventory or quote. The implied usage is still strong for a search tool.

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