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Auto-rig an unrigged humanoid

asset_auto_rig

Idempotent

Fit an 18-joint humanoid skeleton to an unrigged local GLB by measured geometry landmarks, bind smooth or rigid skin weights and publish the skinned GLB under the project root.

Instructions

Fit an 18-joint humanoid skeleton to an unrigged local GLB by measured geometry landmarks, bind smooth or rigid skin weights and publish the skinned GLB under the project root. Preserves an existing rig unless replaceRig is set; ambiguous anatomy returns a landmark correction request instead of a bad rig.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "target": "<target>",
      "output": "<output>",
      "projectRoot": "<projectRoot>"
    },
    "name": "asset_auto_rig"
  }
}

Input Schema

Name	Required	Description	Default
`output`	Yes	Output .glb path under projectRoot.
`target`	Yes	Absolute path to an unrigged humanoid GLB.
`overrides`	No
`replaceRig`	No
`weightMode`	No		smooth
`orientation`	No
`priorDigest`	No
`projectRoot`	Yes	Project root that must contain the output.
`maxInfluences`	No

Output Schema

Name	Required	Description	Default
`bytes`	Yes
`joints`	Yes
`output`	Yes
`sha256`	Yes
`status`	Yes
`target`	Yes
`measure`	Yes
`replaced`	Yes
`landmarks`	Yes
`weightMode`	Yes
`ambiguities`	Yes
`diagnostics`	Yes
`orientation`	Yes
`maxInfluences`	Yes
`alreadyExisted`	Yes
`skinnedVertices`	Yes
`maxNormalizationError`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.1.1Sep 14, 2026

TDQS

A4.2/5.0

Behavior5/5

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

The description adds significant behavioral detail beyond the annotations: it preserves existing rigs unless replaceRig is set, and it returns a landmark correction request on ambiguous anatomy instead of producing a bad rig. These are exactly the kind of behavioral traits an agent needs and that annotations do not provide.

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 dense sentences with no filler. The core operation and the two most important behavioral caveats are front-loaded, and every phrase adds useful information.

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

Completeness2/5

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

This is a complex 9-parameter tool with nested objects, yet the description omits semantics for several optional parameters such as overrides, orientation, priorDigest, and maxInfluences. An output schema exists, so return format is covered, but for a tool this complex the description should provide more guidance on the remaining inputs.

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?

With only 33% schema description coverage, the description must compensate, and it does partially: it clarifies weightMode (smooth/rigid), replaceRig, target/output/projectRoot relationships. However, it leaves overrides, orientation, priorDigest, and maxInfluences semantically unexplained, so an agent would still have to infer their meaning from 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 names a specific operation: fit an 18-joint humanoid skeleton to an unrigged local GLB, bind weights, and publish the skinned GLB. This clearly distinguishes it from sibling tools like asset_inspect_rig or asset_retarget_animations by focusing on auto-rigging unrigged assets.

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?

It clearly says the input must be an unrigged humanoid GLB and explains that an existing rig is preserved unless replaceRig is set, giving a concrete when-to-use/when-not-to-use condition. It does not explicitly name sibling alternatives, but the use case is unambiguous enough for selection.

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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