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multi_image_to_3d_create

Generate a 3D model from 1-4 publicly accessible images. Upload your image URLs to create a textured 3D object with adjustable polycount and topology.

Instructions

Generate a 3D model from multiple images (1-4). Provide publicly accessible URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_urlsYesArray of 1-4 publicly accessible image URLs
ai_modelNoAI model to use
topologyNo'quad' or 'triangle'
target_polycountNoTarget polygon count
should_remeshNoWhether to remesh the output
should_textureNoWhether to generate textures
enable_pbrNoEnable PBR textures
moderationNoScreen input for potentially harmful content
symmetry_modeNoSymmetry mode: 'off', 'auto', or 'on'
save_pre_remeshed_modelNoStore pre-remesh GLB model
pose_modeNoPose mode for characters
image_enhancementNoOptimize input images (default true)
remove_lightingNoRemove highlights and shadows (default true)
texture_promptNoAdditional texture description (max 600 chars)
texture_image_urlNoReference image URL for texture
Behavior2/5

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

With no annotations, the description must disclose behavioral traits but only states core functionality. It omits important details: async nature, processing time, error handling for inaccessible URLs, output format (likely a task ID), and how to retrieve results. The 15-parameter schema suggests complexity that the description fails to address.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, efficient and to the point. It front-loads the core action and primary constraint. While it could benefit from brief bullet points for key behaviors, it avoids unnecessary verbosity.

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?

Given the complexity (15 parameters, no output schema, likely async operation), the description is inadequate. It fails to explain return values, task lifecycle, or common pitfalls. The sibling tools suggest a pattern (e.g., need to poll get endpoints) that is not referenced here.

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?

The input schema provides 100% description coverage for all 15 parameters, reducing the need for description-level elaboration. The description adds no extra meaning beyond what the schema already provides, earning a baseline score of 3.

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 'Generate a 3D model' and specifies the source is 'multiple images (1-4)', which distinguishes it from the sibling 'image_to_3d_create' (single image) and other 3D tools. It is specific and actionable.

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 indicates when to use this tool (need a 3D model from multiple images) and includes a key requirement (publicly accessible URLs). However, it does not explicitly exclude single-image scenarios or mention alternatives like text-to-3d, relying on sibling tool names for differentiation.

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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