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hermoso-ai

Hermoso

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Recast motion

recast_motion

Apply motion from a reference video to a different person or character supplied as an image. The video drives movement; the image provides identity.

Instructions

Motion transfer: re-perform a reference video's motion with a different person/character (supply their image). The reference clip drives the movement; the image supplies the identity. Paid render.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesthe actor/character image URL (who should appear)
videoYesthe reference video whose motion to re-perform
promptNooptional scene/style guidance
orientationNowhich aspect to keep: the video's (default) or the image's

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNothe raw job result payload (e.g. images[] for carousel template ads)
urlNothe served URL of the finished media (absent/null while still rendering)
jobIdNothe render job id — poll get_job with this id to resume or inspect
modelNothe product-facing label of the model that rendered it
stillRenderingNotrue when the render is still in progress — keep polling get_job with jobId
Behavior3/5

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

Annotations are all false and contain no hints. The description adds value by mentioning 'paid render', which alerts the agent to cost. However, it does not disclose whether the operation is destructive, whether it modifies inputs, or any rate limits. Since annotations provide no safety profile, the description carries the full burden but only partially addresses it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences (31 words) with no wasted text. It front-loads the core purpose and immediately distinguishes the inputs. Every sentence serves a clear function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, return values are not needed. The description covers the essential workflow and highlights the 'paid render' aspect. It lacks explicit error handling or constraints (e.g., video format), but for a tool with clear inputs and an output schema, it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds context beyond schema by explaining that 'image' supplies identity and 'video' drives motion. This helps clarify the roles of the required parameters. The optional 'prompt' and 'orientation' are not detailed, but the schema already uses descriptive names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('re-perform', 'supply') and clearly identifies the resource ('motion transfer with a reference video and identity image'). It differentiates from sibling tools like 'generate_video' (text-to-video) and 'generate_avatar' (avatar creation).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states when to use the tool (to re-perform motion) and notes that it is a 'paid render', implying cost. However, it does not explicitly exclude use cases where other tools (e.g., 'finish_video') might be more appropriate, nor does it specify prerequisites like image quality.

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

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