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

No annotations are provided, so the description carries the burden. It discloses the internal multi-model image chain with automatic fallback and the cost of 5 credits per fabric variant, but it omits output format, side effects (e.g., whether it modifies anything), or auth requirements.

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?

Three sentences, each earning its place: purpose, technical behavior, and cost. The phrase 'best-in-class' is marketing filler but does not undermine conciseness.

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?

The description gives purpose, cost, and a hint about internal fallback, but without an output schema it fails to explain what the tool returns (e.g., image URLs). It also does not mention prerequisites like checking credits, even though a sibling tool exists for that.

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%, with all six parameters fully described in the input schema. The description only restates fabric examples and cost, adding no meaningful parameter semantics beyond what the schema already provides.

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 ('Visualize') and resource ('a garment design in different fabrics and materials'), clearly distinguishing this from sibling tools like generate_colorways (colors) and generate_multi_angle (angles). The purpose is unambiguous and front-loaded.

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 use case is clear: when you want to see a garment in various fabric finishes, this tool is for you. However, it does not explicitly mention alternatives or when not to use it, though the sibling list makes the differentiation obvious.

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