Start a Fashio generation job

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

With no annotations, the description carries the full behavioral burden. It discloses the key side effect: 'Deducts diamonds from the account that owns the API key,' and the asynchronous behavior: returns with status 'processing' and requires polling. It does not cover error or failure behavior, but the most important operational risks are stated.

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?

Four sentences deliver the core action, cost, prerequisite, async workflow, and template alternative with no filler. The most important operational facts are front-loaded, and every sentence earns 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?

For a complex 13-parameter creation tool with nested objects and no output schema, the description covers the main workflow and conditional parameters. It omits the exact response identifier and failure/error semantics, but the schema handles per-parameter details and the description points to the right sibling tools for prerequisites and polling.

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?

The schema already provides 100% coverage of all 13 parameters, so the baseline is 3. The description adds value beyond the schema by explaining that toolId validity and images requirements come from list_models, and that templateSetId fills in multiple style-related defaults. This cross-tool context makes parameter selection easier.

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 starts with a specific verb and resource: 'Starts an AI fashion generation job.' It clearly differentiates this creation tool from the polling tool by stating 'Returns immediately with status processing — poll get_generation for the result.' This makes the tool's role in the workflow unambiguous.

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 explicitly says to call list_models first for valid toolId values and required images keys, and to poll get_generation for the result. It also offers a clear alternative: pass templateSetId instead of manually choosing fashion_model/venue/pose. This directly guides tool selection and invocation order.

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