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

The description adds important behavioral details beyond the annotations: it is paid ($0.10 per image), runs asynchronously, returns a generation_id, and instructs the caller to poll until status is 'completed'. This meaningfully clarifies what an agent should expect when invoking the tool.

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 short sentences, each carrying distinct information: the visual effect, the cost, and the asynchronous workflow. There is no filler or repetition of schema contents.

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 an asynchronous image-generation tool with no output schema, the description adequately explains how to retrieve the result by polling get_generation. It could mention failure modes or estimated runtime, but the essential call-and-retrieve flow is complete.

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 parameter meanings are already documented in the schema. The description adds no detail about the color or style parameters beyond what the schema provides, which matches the baseline expectation.

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?

‘See yourself with braces’ clearly communicates the tool's effect: it transforms a photo to add braces. It implies the input is an image and is distinct from the sibling tools, though it stops short of an explicit verb+resource phrasing like 'applies braces to an image'.

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 usage context: the tool is asynchronous and must be polled via get_generation. However, it provides no guidance on when to choose this tool over sibling image filters/editors, so selection criteria are only implied by the name and effect.

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