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IzzyFuller

intentional-masking

by IzzyFuller

render_video

Turn audio into a video of an avatar speaking with lip sync. Add optional body animations and adjust camera, lighting, and background.

Instructions

Render an avatar speaking with lip sync from audio, optionally with body animations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
animationsNoOptional body animation timeline
audio_pathYesPath to audio file (from TTS like info-dump)
backgroundNoBackground color (hex)#1a1a2e
avatar_pathYesPath to the avatar .glb file
output_pathNoOptional output path (default: auto-generated in output/)
camera_presetNoCamera angle presetmedium
lighting_presetNoLighting stylesoft
Behavior2/5

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

With no annotations provided, the description carries full responsibility for disclosing behavioral traits. It only states what the tool does, not side effects (e.g., output file creation), processing requirements, or constraints (e.g., avatar format, animation requirements). This is a significant gap for a rendering tool.

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 a single, front-loaded sentence with no wasted words. It immediately states the action, subject, and key parameter relationships, making it highly scannable for an agent.

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

Completeness3/5

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

Given the 7-parameter schema with full descriptions, the description doesn't need to repeat parameter details. However, it omits the tool's output behavior (e.g., generates a video file) and any mention of the rendering process's cost or complexity. It's adequate for a simple tool, but not comprehensive for a rendering operation with no output schema.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by clarifying relationships: audio drives lip sync, animations are optional, and the avatar is the subject. This contextual framing helps understand the key parameters beyond their individual schema descriptions.

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 uses a specific verb ('Render') and clearly identifies the resource ('an avatar speaking with lip sync from audio'). It also mentions the optional body animations, which distinguishes it from a simple audio-to-video tool. Even without siblings, the purpose is unambiguous.

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

Usage Guidelines3/5

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

The description implies the tool is used when you need to generate an avatar video from audio, but it does not state prerequisites, alternatives, or explicit when-to-use/when-not-to-use guidance. The schema hints at audio source (TTS) but the description itself provides no direct usage context.

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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