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BlenderMCP

generate_hyper3d_model_via_text

Create 3D models in Blender by describing them in text, generating assets with built-in materials that can be imported and scaled as needed.

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

  • Main handler function for the 'generate_hyper3d_model_via_text' tool. Decorated with @mcp.tool() for automatic registration. Processes text prompt and optional bbox_condition, submits job to Hyper3D via Blender connection, returns task UUID and subscription key or error.
    @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 normalize and validate the bbox_condition parameter into a list of integers scaled to 0-100 range.
    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
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. It discloses that the model has built-in materials, normalized size (requiring potential rescaling), and returns a success/failure message. However, it lacks details on permissions, rate limits, processing time, or error conditions, which are important for a generative 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 well-structured with a purpose statement, behavioral details, and a parameters section. It's front-loaded and avoids redundancy, though the parameters section could be more integrated into the flow rather than listed separately.

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?

For a generative tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is moderately complete. It covers purpose, key behaviors, and parameter meanings, but lacks output details (beyond success/failure), error handling, or integration context with Blender or sibling tools.

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 description coverage is 0%, so the description must compensate. It explains 'text_prompt' as a short English description and 'bbox_condition' as an optional list of 3 floats controlling length, width, height ratios. This adds meaningful semantics beyond the bare schema, though it could specify format details like float ranges or units.

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 generates a 3D asset using Hyper3D based on a text description and imports it into Blender. It specifies the resource (3D asset) and verb (generate and import), but doesn't explicitly differentiate from sibling 'generate_hyper3d_model_via_images' beyond the 'via_text' naming.

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 for generating 3D assets from text prompts, with optional bbox conditioning. However, it doesn't explicitly state when to use this vs. alternatives like 'generate_hyper3d_model_via_images' or other asset download tools, nor does it mention prerequisites or exclusions.

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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