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

The description adds meaningful behavioral context beyond the annotations: the operation is asynchronous, returns a generation_id, requires polling, and costs $0.10 per image. It does not detail failure modes or exact result format, but the core non-obvious behavior is disclosed.

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 two sentences with no filler. It front-loads the core purpose, then the cost and the critical asynchronous workflow, making every sentence earn its place.

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 covers the essential call sequence, required/optional inputs are fully in the schema, and the asynchronous continuation is explained. It is slightly light on final output details, but there is no output schema and the polling instruction sufficiently orients the agent.

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 input schema already documents style, persona, and input_image. The description adds no extra parameter-level meaning, which is acceptable given the complete schema; the baseline of 3 applies.

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 identifies the tool's resource and output: stylized selfies and avatars, and implies the generation verb from the title. It is distinct enough from unrelated siblings, though it does not explicitly differentiate from close alternatives like ai_anime_filter or face_swap.

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 clear procedural context: run asynchronously, receive a generation_id, and poll get_generation until status is completed. It does not explicitly state when to prefer this tool over alternatives, but the stylized selfie/avatar scope provides a clear usage context without exclusions.

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