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generate_hyper3d_model_via_text

Create 3D models in Blender using text descriptions, generating assets with built-in materials and normalized sizing for immediate use.

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

Implementation Reference

  • The handler function for generating a 3D model from a text prompt via Hyper3D.
    def generate_hyper3d_model_via_text(
        ctx: Context,
        text_prompt: str,
        bbox_condition: list[float]=None
    ) -> str:
        """
        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.
        """
        try:
            blender = get_blender_connection()
            result = blender.send_command("create_rodin_job", {
                "text_prompt": text_prompt,
                "images": None,
                "bbox_condition": _process_bbox(bbox_condition),
            })
            succeed = result.get("submit_time", False)
            if succeed:
                return json.dumps({
                    "task_uuid": result["uuid"],
                    "subscription_key": result["jobs"]["subscription_key"],
                })
            else:
                return json.dumps(result)
        except Exception as e:
            logger.error(f"Error generating Hyper3D task: {str(e)}")
            return f"Error generating Hyper3D task: {str(e)}"
  • The MCP tool registration decorator for generate_hyper3d_model_via_text.
    @mcp.tool()
Behavior4/5

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

With no annotations provided, the description carries the full burden and discloses several key behavioral traits: the asset is automatically imported into Blender, has built-in materials, has normalized size requiring potential rescaling, and returns a success/failure message. It lacks explicit mention of generation time or service availability requirements, but covers the essential operational characteristics.

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 front-loaded with the core function, followed by material and scaling properties that earn their place (operational guidance), then a clear Parameters section. No sentences are wasted; structure is logical and efficient for a 2-parameter tool.

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

Completeness5/5

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

Given the tool's moderate complexity (2 simple parameters, no output schema), the description is complete. It explains the generation process, Blender integration, result properties, parameter details, and return value behavior (success/failure message), covering all gaps left by the missing annotations and output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by documenting both parameters: text_prompt requires an English short description, and bbox_condition is optional but must be a list of 3 floats controlling Length/Width/Height ratios. This provides complete semantic meaning missing from the schema.

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 core action ('Generate 3D asset using Hyper3D'), the input method ('giving description of the desired asset'), and the destination ('import the asset into Blender'). The phrase 'giving description' effectively distinguishes this from the sibling tool generate_hyper3d_model_via_images.

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?

While the name and description imply this is for text-based generation (vs. image-based), there is no explicit guidance on when to choose this over generate_hyper3d_model_via_images or other 3D acquisition tools like download_sketchfab_model. Usage is implied but not explicitly contrasted with alternatives.

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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