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retexture_to_youtube

Re-texture an existing 3D model, render a turntable animation, and upload it as a YouTube video with configurable privacy, tags, and resolution.

Instructions

One-shot: re-texture an existing model -> turntable -> YouTube video. Always returns a dict.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fpsNo
tagsNo3d,meshy,retexture
titleYes
framesNo
privacyNounlisted
timeoutNo
durationNo
model_urlNo
enable_pbrNo
resolutionNo
category_idNo22
descriptionNo
input_task_idNo
made_for_kidsNo
image_style_urlNo
text_style_promptNo
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It transparently reveals that the call is a multi-step pipeline and that it 'Always returns a dict,' which is useful. However, it does not mention the long-running/external side effects of uploading to YouTube, authentication requirements, or failure behavior, so transparency is only partial.

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 tight sentence plus one useful guarantee, with no filler or repetition. The pipeline is front-loaded and every word carries 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?

For a tool with 16 parameters, no output schema, and no annotations, this is too sparse. It omits how the retexture is specified (style/prompt vs reference), what the returned dict contains, how long the operation takes, and how the final upload behaves, leaving an agent under-equipped for correct invocation.

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 description coverage is 0%, and the description names no parameters. The phrase 'existing model' hints that input is via model_url or input_task_id, and 'YouTube video' implies title/upload-related fields, but the remaining 14 parameters (e.g., fps, frames, privacy, enable_pbr, image_style_url, text_style_prompt) are left entirely unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific composite operation: take an existing model, re-texture it, render a turntable, and produce a YouTube video. The 'One-shot' framing and arrow pipeline differentiate it from piecemeal sibling tools like render_turntable, frames_to_video, and upload_to_youtube, though it does not name them explicitly.

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 phrase 'One-shot' implies this tool is for performing the whole retexture-to-YouTube pipeline in a single call rather than chaining individual siblings, but the description gives no explicit when-to-use or when-not-to-use guidance. No alternatives are named, leaving the routing decision to inference.

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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