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Retarget an animation onto a rigged asset

animate_asset

Apply a preset animation to an already-rigged 3D asset, triggering an asynchronous job to fetch later. Prevents billing errors by rejecting unrigged source jobs.

Instructions

SPENDS 3D CREDITS. Applies a preset animation to an asset that has ALREADY been rigged with rig_asset. Each animation costs separately. ASYNCHRONOUS: poll get_asset_job, then download_asset. Refuses when the source job was not a rig, because retargeting an unrigged model produces nothing while still being billed. NOTE: the provider task name is taken from published docs and is UNVERIFIED live.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
animationYesProvider preset animation name, e.g. "walk".
outFormatNoglb
assetJobIdYesA job produced by rig_asset.
Behavior5/5

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

Beyond annotations (readOnlyHint=false, idempotentHint=false), the description discloses critical behaviors: it 'SPENDS 3D CREDITS', it is asynchronous (requiring polling), it refuses for non-rig jobs, and it flags the provider task name as 'UNVERIFIED live'. These operational and trust details are not present in the annotations, giving the agent a complete picture of consequences and limitations.

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?

Every sentence in the description carries essential content: cost, purpose, asynchronous workflow, failure condition, and a trust caveat. It is front-loaded with the most urgent constraint (credit spend) and structured so that prerequisites and post-conditions are immediately clear. No filler or redundant phrasing.

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

Completeness5/5

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

For a 3-parameter tool with no output schema, the description covers all necessary usage context: the prerequisite rigging, the asynchronous flow with specific follow-up tools, the cost model, the refusal behavior for invalid inputs, and a warning about unverified provider metadata. An agent can confidently invoke it correctly without additional information.

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

Parameters3/5

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

Schema description coverage is 67% (assetJobId and animation have descriptions; outFormat only has enum/default). The description adds minimal param-specific meaning: it reinforces that assetJobId must be a rig job and notes cost per animation, but these are largely operational or redundant with the schema (which already says 'A job produced by rig_asset'). The baseline of 3 applies since coverage is moderate and the description only slightly supplements the schema.

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 states a specific verb ('Applies'), a resource ('a preset animation to an asset'), and a critical prerequisite ('ALREADY been rigged with rig_asset'). It clearly distinguishes this tool from other asset-creation tools by requiring a prior rig job, making its role in the pipeline unambiguous.

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

Usage Guidelines5/5

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

It explicitly states the precondition (must be rigged), the follow-up workflow ('poll get_asset_job, then download_asset'), and a refusal condition ('Refuses when the source job was not a rig'). This tells an agent exactly when to use it, what to do after, and when it will fail, leaving no ambiguity about its place relative to siblings like rig_asset or download_asset.

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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