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hermoso-ai

Hermoso

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Recast motion

recast_motion

Re-perform a reference video's motion with a different person or character by supplying their image. The video drives movement, the image determines 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
Install Server

TDQS

A3.9/5.0
Behavior3/5

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

Annotations provide no safety hints (all false), so the description carries the burden. It discloses that it is a paid render, which is a meaningful behavioral trait (cost). It also clarifies the input roles (motion vs identity) but does not mention output format, async behavior, or other side effects. While it adds some value beyond annotations, it leaves significant behavioral gaps for a generation tool.

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 three sentences, with the core purpose front-loaded ('Motion transfer'), followed by a clarifying analogy and a cost warning. Every sentence earns its place without redundancy. It is appropriately sized and easy to parse.

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

Completeness3/5

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

For a tool with 4 parameters and no output schema, the description covers the core concept and input roles but omits output expectations (e.g., whether it returns a video URL or a job ID) and potential prerequisites or limitations. Given the complexity of a paid generation task, more detail would be helpful, though the schema covers parameter specifics. This is adequate but not 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 the baseline is 3. The description adds conceptual clarification beyond the schema: 'The reference clip drives the movement; the image supplies the identity' reinforces the roles of the video and image parameters. This goes beyond the schema's literal descriptions and helps the agent understand the relationship, so a 4 is warranted.

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 clearly states the tool does motion transfer: it re-performs a reference video's motion with a different person/character, using the image for identity. This is a specific verb-resource pair that distinguishes it from other video tools like generate_video or generate_avatar. The mention of 'Paid render' adds a crucial operational detail. The description leaves no ambiguity about what the tool does.

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

Usage Guidelines3/5

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

The description implies the use case: when you have a reference video motion and want to apply it to a different subject. However, it does not explicitly state when to prefer this over alternatives, nor does it provide exclusions or conditions (e.g., when to use generate_video instead). The context is clear but there is no explicit guidance on alternative tool selection, so it earns a mid-range score.

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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