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vocal_animation

Generate viseme sets that lip-sync a portrait to any line of text. Use a stored character portrait or inline image, then poll the returned job ID for results.

Instructions

Generate the set of mouth positions ('visemes') that lets a portrait be lip-synced to any line of text. This is the only talking-animation step that costs generations — pay it once per expression, then talking_gif and lip_sync are free and unlimited. Provide either character_id (uses the character's stored portrait, set via set_character_portrait, and saves the result onto it) or an inline portrait image (max 256x256, result returned inline). Returns a job_id — poll get_vocal_animation_job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moodNoExpression held on the face throughout (default 'neutral'). Call once per expression you wantneutral
seedNoSeed for deterministic generation (default 0)
portraitNoGenerate from this image instead and store nothing — mouth positions come back inline. Max 256x256. Mutually exclusive with character_id
character_idNoGenerate from this character's stored portrait and save the result onto it. Required to later use character_id with talking_gif. Mutually exclusive with portrait
viseme_countNoHow many mouth positions to generate (default 7). 3 for tiny portraits, 12 for large close-ups. Must be the same for every expression on one character
no_backgroundNoReturn frames with a transparent background (default true)
Behavior5/5

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

With no annotations, the description carries the full burden. It discloses the async behavior (returns a job_id to poll), the cost model (only step that costs generations), storage implications (saves result onto the character when using character_id), and size constraints (max 256x256). These go beyond the structured schema and provide key operational context.

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 four sentences, each earning its place: purpose, cost model, input options, and async result handling. It is front-loaded with the primary action and avoids fluff or repetition.

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 complex async tool with two input modes, the description covers purpose, cost, prerequisites, input selection, storage behavior, and polling. It gives enough to safely invoke the tool even without prior knowledge of the talking-generation pipeline.

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?

The schema already describes all six parameters thoroughly (100% coverage), so the baseline is 3. The description adds meaningful context by explaining the mutual exclusivity of portrait and character_id, the save behavior of character_id, and the cost relationship to the mood parameter ('pay it once per expression'). It doesn't explain seed or viseme_count beyond schema, but those are well-defined in 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 opens with a specific verb and resource: 'Generate the set of mouth positions (visemes) that lets a portrait be lip-synced to any line of text.' It clearly distinguishes this from sibling tools like talking_gif and lip_sync by framing it as the generation step that makes those downstream tools possible.

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?

Explicitly states when to use this tool: 'This is the only talking-animation step that costs generations — pay it once per expression, then talking_gif and lip_sync are free and unlimited.' It also gives guidance on choosing between character_id and inline portrait, and mentions that set_character_portrait is a prerequisite for the character_id path.

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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