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image_to_3d

Convert 2D images into 3D models using reference photos. Upload local files or public URLs to generate textured 3D assets for visualization, prototyping, or digital content creation.

Instructions

Generate a 3D model from a reference image. Provide a local file path or public URL. This is an async operation — use task_status to poll progress and download_model to retrieve the result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imagePathNoLocal file path to the reference image. Mutually exclusive with imageUrl
imageUrlNoPublic URL of the reference image. Mutually exclusive with imagePath
modelVersionNoModel version. Defaults to latest
faceLimitNoTarget polygon face count
textureQualityNoTexture quality: standard or detailed
orientationNoModel orientation: default or align_image

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskIdYes
statusYes
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's an async operation (not immediate return), requires polling via task_status, and needs a separate download step. However, it doesn't mention potential limitations like file size restrictions, supported image formats, or error conditions.

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 perfectly front-loaded with the core purpose in the first sentence, followed by essential usage guidance. Every sentence earns its place: the first defines the tool, the second specifies input options, and the third explains the async workflow. No wasted words or redundant information.

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?

Given the tool's complexity (async operation with 6 parameters) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the core workflow and distinguishes from siblings, but could benefit from mentioning typical use cases or performance expectations given the async nature.

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 100%, so the schema already documents all 6 parameters thoroughly. The description adds minimal value beyond the schema - it mentions providing 'a local file path or public URL' which corresponds to imagePath and imageUrl parameters, but doesn't provide additional context about parameter interactions or usage patterns.

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 specific action ('Generate a 3D model') and resource ('from a reference image'), distinguishing it from siblings like text_to_3d (text input) and multiview_to_3d (multiple images). It precisely defines the tool's function without being vague or tautological.

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?

The description explicitly states when to use this tool (for generating 3D models from single images) and provides clear alternatives for related operations: use task_status to poll progress and download_model to retrieve results. It also distinguishes from siblings by specifying the input type (image vs. text or multiple views).

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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