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generate_hyper3d_model_via_text

Generate a 3D model from a text description and import it directly into Blender with built-in materials. Optionally specify bounding box ratios.

Instructions

Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender. The 3D asset has built-in materials. The generated model has a normalized size, so re-scaling after generation can be useful.

Parameters:

  • text_prompt: A short description of the desired model in English.

  • bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.

Returns a message indicating success or failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
text_promptYes
bbox_conditionNo
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the asset has built-in materials, normalized size (suggesting post-generation re-scaling), and that it imports into Blender. Missing details like time duration or side effects, but these are reasonable omissions.

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?

Three tight sentences plus parameter descriptions. Every sentence adds value: purpose, material info, normalization hint. No filler.

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?

For a tool with 2 params and no output schema, the description covers key aspects: generation process, importing, materials, size. Missing details like typical generation time or error messages, but these are not critical for basic usage.

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 0%, so description compensates well: text_prompt is 'short description...in English', bbox_condition is 'list of floats of length 3 controlling ratio between Length, Width, Height'. This adds essential context beyond the schema's type info.

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 3D asset using Hyper3D by giving description' and 'import the asset into Blender'. It specifies verb and resource, and distinguishes from siblings like 'generate_hyper3d_model_via_images' which uses images instead of text.

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 usage by stating the text prompt parameter is required and mentions optional bbox_condition. However, it does not explicitly state when to use this tool versus alternatives (e.g., when to use image-based generation) or any prerequisites.

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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