generate_digital_human

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

With no annotations provided, the description carries the full responsibility for behavioral disclosure. It adds valuable context such as the exact model, audio format and duration limits, resolution choices, prompt language constraints, and a warning that this API uses CVSubmitTask/CVGetResult rather than the normal interface.

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 concise and well-structured with bullet points for parameters and a clearly separated usage note. Every sentence provides useful information, with no filler or repetition.

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 tool's purpose, all parameters, important constraints, and the non-standard API flow. Since an output schema is present, return values need not be explained, though it stops short of detailing how to actually use CVGetResult to retrieve results.

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%, but the description fully compensates by explaining all four parameters: portrait_url (person/anime/pet image), audio_url (WAV/MP3 under 60 seconds), resolution (720/1080 default 1080), and prompt (optional, multilingual, max 300 characters). This adds substantial meaning beyond the bare schema.

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 that this tool generates a digital human talking video by driving a portrait image's lips and actions with audio, and it names the specific model (OmniHuman 1.5). This specific verb+resource combination distinguishes it from sibling tools like generate_video or image_to_video.

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 implied by the phrase '数字人生成' (digital human generation) and the inputs described, but there is no explicit guidance on when to choose this tool over alternatives such as image_to_video or imitate_motion. No when-not-to-use or alternative tool references are provided.

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