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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 easy integration.

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 core handler function for the 'generate_hyper3d_model_via_text' tool. It is registered via @mcp.tool() decorator. Takes text_prompt and optional bbox_condition, processes bbox with _process_bbox helper, sends 'create_rodin_job' command to Blender connection, and returns JSON with task details 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 handler to process and normalize the bbox_condition input 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
  • Input schema defined by function parameters type hints and docstring describing text_prompt (str) and optional bbox_condition (list[float]). Output is str (JSON or error message).
    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.
        """
  • The @mcp.tool() decorator registers this function as an MCP tool.
    @mcp.tool()
    def generate_hyper3d_model_via_text(
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 rescaling), and returns a success/failure message. However, it omits details like processing time, error conditions, or Blender import specifics, leaving behavioral gaps.

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 front-loaded with the core purpose, followed by key features and parameters. Sentences are efficient, but the parameter section could be integrated more smoothly. Overall, it avoids unnecessary verbosity.

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 no annotations and no output schema, the description provides basic purpose and parameters but lacks details on return values beyond success/failure, error handling, or integration with sibling tools like 'import_generated_asset.' For a generative tool with 2 parameters, it's adequate but incomplete.

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 ratio. This adds meaningful context beyond the bare schema, though it could detail format constraints more.

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 specific action ('Generate 3D asset using Hyper3D'), the resource ('3D asset'), and distinguishes from siblings by specifying text-based generation (vs. image-based 'generate_hyper3d_model_via_images'). It also mentions importing into Blender, which is a distinct outcome.

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 through the phrase 'by giving description of the desired asset,' but lacks explicit guidance on when to use this tool versus alternatives like 'generate_hyper3d_model_via_images' or 'download_polyhaven_asset.' No exclusions or prerequisites are mentioned.

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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