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Auto-rig a character PNG

riv_rig_character

Create a rigged Rive character from a PNG: cutout ears/tail, head-tilt mesh, eyelid blink, idle and happy animations, and a state machine trigger.

Instructions

One call: character PNG -> fully rigged .riv with cutout parts (ears/tail via polygons), 2-bone head-tilt mesh (seamless), vector eyelid blink, idle + happy animations, and a state machine with a 'happy' trigger. Returns a preview. Fine-tune afterwards with riv_edit or riv_studio.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eyesNoEye rects in image px coords (generates blink overlays)
partsNoNamed cutout parts, e.g. {earL, earR, tail}. Names containing 'ear' attach to the head
outPathYesOutput .riv path
pngPathYesCharacter PNG (transparent background recommended)
furColorNoEyelid/patch color matching the fur (default #f8eee2)
headRatioNoTop fraction of the image that is 'head' (default 0.45)
artboardWidthNo
artboardHeightNo
backgroundColorNo
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses detailed output behavior: cutout parts via polygons, 2-bone head-tilt mesh, vector eyelid blink, idle/happy animations, state machine with 'happy' trigger, and a preview return. This is substantial, though it doesn't mention failure modes, overwrite behavior, or input requirements beyond 'transparent background recommended'.

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?

Two sentences, highly information-dense and front-loaded. The first sentence packs the full transformation and output features; the second sentence gives actionable follow-up alternatives. No wasted words.

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?

For a complex 9-parameter tool with nested objects and no output schema, the description covers the core pipeline, output features, and sibling tools for refinement. It doesn't mention the nature of the 'preview' or edge cases, but the main workflow is clearly communicated. The schema handles parameter details.

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%, and the description adds some semantic context (e.g., 'ears/tail via polygons' hints at the 'parts' parameter, 'head-tilt mesh' links to headRatio/eyes). However, it doesn't explain parameters like artboardWidth/Height or backgroundColor, which remain undocumented in schema. It adds value but doesn't fully compensate for gaps.

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's function: 'character PNG -> fully rigged .riv' with specific features (cutout parts, head-tilt mesh, blink, animations, state machine). It distinguishes itself from siblings by mentioning 'Fine-tune afterwards with riv_edit or riv_studio', positioning this as the one-call auto-rigging tool.

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 implies usage for initial rigging from a PNG, with explicit guidance to use 'riv_edit or riv_studio' for fine-tuning. This gives clear context and alternatives, though it doesn't state exclusions (e.g., for non-PNG inputs) or when to prefer other tools like riv_import_svg.

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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