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generate_hyper3d_model_via_text

Create 3D models in Blender using text descriptions. Generate assets with built-in materials and normalized sizing for easy integration into 3D scenes.

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 the 'generate_hyper3d_model_via_text' tool. It is decorated with @mcp.tool() for registration and implements the logic to create a Hyper3D Rodin generation job using the provided text prompt and optional bbox_condition by sending a command to the Blender connection.
    @mcp.tool()
    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)}"
  • Helper function used by the tool to process and normalize the bbox_condition parameter into a list of integers scaled relative to the maximum value times 100.
    def _process_bbox(original_bbox: list[float] | list[int] | None) -> list[int] | None:
        if original_bbox is None:
            return None
        if all(isinstance(i, int) for i in original_bbox):
            return original_bbox
        if any(i<=0 for i in original_bbox):
            raise ValueError("Incorrect number range: bbox must be bigger than zero!")
        return [int(float(i) / max(original_bbox) * 100) for i in original_bbox] if original_bbox else None
  • The docstring provides the schema definition including parameter descriptions and types for the tool.
    """
    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.
    """
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: the 3D asset has built-in materials, the model has normalized size (suggesting rescaling may be needed), and it returns a success/failure message. However, it lacks details on permissions, rate limits, error conditions, or what 'import into Blender' entails operationally. The description compensates partially but leaves gaps for a mutation tool.

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 appropriately sized and front-loaded, starting with the core functionality. Each sentence adds useful information: generation method, material properties, sizing note, parameter details, and return value. There's minimal waste, though the parameter section could be integrated more seamlessly. Overall, it's efficient and well-structured.

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

Completeness3/5

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

Given the complexity (a generative 3D modeling tool with 2 parameters, no annotations, and no output schema), the description is moderately complete. It covers the basic operation, parameters, and return message, but lacks details on behavioral aspects like error handling, Blender integration specifics, or performance expectations. For a tool with no structured metadata, it provides a foundation but leaves room for more context.

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?

The description provides meaningful semantics for both parameters: 'text_prompt' is described as 'A short description of the desired model in English,' and 'bbox_condition' as 'Optional... Controls the ratio between [Length, Width, Height] of the model.' With 0% schema description coverage, this adds significant value beyond the bare schema, clarifying purpose and constraints, though it doesn't specify exact formats or units for the bbox_condition list.

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 clearly states the tool's purpose: 'Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.' It specifies the verb (generate), resource (3D asset), and method (via text prompt), though it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_images' beyond the input method. The mention of importing into Blender adds useful context.

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 tool generates 3D assets from text prompts and imports them into Blender, suggesting it's for text-to-3D generation workflows. However, it doesn't explicitly say when to use this tool versus alternatives like 'generate_hyper3d_model_via_images' or 'download_polyhaven_asset', nor does it provide exclusions or prerequisites. The guidance is contextual but not comprehensive.

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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