imitate_motion

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

With no annotations provided, the description carries the transparency burden. It discloses a notable behavioral trait: '输出视频约需 3～5 分钟（10 秒视频约需 180 秒）' (output video takes approximately 3–5 minutes), indicating this is a long-running operation. It also specifies input format and size limits, which inform the agent of constraints. However, it does not mention side effects, authentication, or whether the operation is asynchronous, though output schema likely covers return details.

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 compact and front-loaded with the purpose, followed by model name, parameter details in a bullet-like format, and latency estimate. Each sentence adds value with no redundancy. It is appropriately sized for a moderately complex tool.

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?

Given the tool complexity (video generation with two inputs), the description covers purpose, inputs, constraints, and expected latency. It does not explicitly mention prerequisites like whether the image must contain a person's face or the video must contain human motion, but these are implied by '目标人物图片' and '动作参考视频'. Output schema likely handles return format, so this description is sufficient for an agent to select and invoke correctly.

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 coverage is 0%, so the description must fully explain parameters. It does: 'image_url: 目标人物图片 URL（JPEG/PNG，480×480 以上，1920×1080 以内）' and 'video_url: 动作参考视频 URL（MP4/MOV/WEBM，最长 30 秒）' provide semantics, formats, and constraints. This fully compensates for 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 the tool's function: '动作模仿 2.0：提取参考视频中的动作/表情/口型，迁移到目标人物图片上' (extract actions/expressions/lip sync from a reference video and transfer to a target person image). This specific verb+resource pairing distinguishes it from siblings like image_to_video or generate_digital_human by focusing on motion transfer from a video to a person image. It also names the model, reinforcing the purpose.

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 provides clear context: the tool is used when you have a reference video and a target person image, and it lists the required inputs (image_url, video_url) with constraints (formats, sizes, max duration). It does not explicitly list when to avoid using it or name alternatives, but the usage scenario is evident from the description.

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