try_on_image

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

Annotations provide no behavioral hints (readOnlyHint=false, destructiveHint=false), so the description carries full burden. It discloses recommendations (up to 6 garments, turbo not for >4), experimental flags (prompt, reference_pose), and defaults, but omits details on failure modes, rate limits, or exact output format.

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 brief and well-structured: a one-sentence summary followed by a bullet-like list of parameters. Every sentence adds value, with no redundancy or fluff.

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?

Despite 10 parameters and no output schema, the description explains each parameter well and offers usage tips. However, it lacks an explicit description of what the tool returns (e.g., an image URL) and does not specify input format requirements (e.g., valid file types).

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 0%, so the description must fully explain parameters. It does so with clear explanations for all 10 parameters, including defaults, recommendations, and experimental notes—adding significant meaning beyond raw schema property names.

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 it virtually fits garments onto a person's photo using Pruna AI. The verb 'fit' and resource 'garments onto person's photo' are specific, and the tool is distinct from siblings like edit_image or generate_image due to its focused try-on functionality.

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 implicitly defines usage for virtual try-on but does not explicitly state when to use this tool over alternatives or when not to use it. Sibling tools like edit_image or generate_image are not mentioned, so an agent lacks guidance on trade-offs.

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