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Generate a skeleton and skin weights

rig_asset

Adds a skeleton and skin weights to a modeled asset so it can be animated. Provide an asset job ID, choose a skeleton convention, and get a rigged GLB or FBX to use in animation.

Instructions

SPENDS 3D CREDITS. Rigs an existing generated asset — builds a skeleton and skin weights so it can be animated. Takes an asset job that already produced a model. ASYNCHRONOUS: poll get_asset_job, then download_asset. Rig before animating; retargeting an unrigged model wastes the credits. NOTE: the provider task name is taken from published docs and is UNVERIFIED live.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specNoWhich skeleton convention to target.humanoid
outFormatNoglb
assetJobIdYesA job whose 3D task produced a model.
Behavior5/5

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

The description discloses several behavioral traits beyond the annotations: it spends 3D credits (cost), it is asynchronous requiring polling of get_asset_job and download_asset, and it notes that the provider task name is unverified. These are critical operational details that annotations (readOnlyHint=false, openWorldHint=true) do not capture. The description adds substantial transparency.

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 earns its place: the credit warning is front-loaded, the async workflow is stated, and the prerequisite and warning about wasted credits are included. It is information-dense but not bloated, with a clear logical flow from cost to action to next steps.

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?

Given the complexity (asynchronous, credit-spending, dependency on prior job), the description covers all essential aspects: what it does, the prerequisite, the async polling pattern, and the cost implication. It even flags the unverified provider name. For a tool with no output schema, this is nearly complete for an agent to use correctly.

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

Parameters2/5

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

Schema coverage is 67%, leaving outFormat undocumented. The description does not clarify what outFormat options are or how spec affects the rigging, beyond what the schema already states for assetJobId. While the description reinforces that assetJobId must reference a completed model, it adds minimal value to parameter understanding. The missing outFormat explanation is a clear gap.

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 action (rigs), the resource (an existing generated asset), and the outcome (builds skeleton and skin weights). It distinguishes from siblings like animate_asset and retopologize_asset by framing the purpose as enabling animation. The verb-resource combination is specific and unambiguous.

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 gives explicit context: it must be used on an asset job that already produced a model, and it should precede animation ('Rig before animating'). It also warns about wasting credits by retargeting an unrigged model, which implies when this tool is the right choice. It does not explicitly name alternatives like animate_asset, but the sequencing guidance provides adequate usage direction.

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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