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generate_hyper3d_model_via_text

Create 3D models from text descriptions and import them directly into Blender with built-in materials. Control model dimensions and scale as needed for your scene.

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 @mcp.tool()-decorated handler function that implements the core logic of the 'generate_hyper3d_model_via_text' tool. It normalizes the bbox_condition using _process_bbox, sends a 'create_rodin_job' command to Blender with the text prompt, and returns the task details or error message.
    @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 bounding box condition 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
  • MCP prompt that provides strategy guidance including instructions on using 'generate_hyper3d_model_via_text' for text-based 3D asset generation.
    @mcp.prompt()
    def asset_creation_strategy() -> str:
        """Defines the preferred strategy for creating assets in Blender"""
        return """When creating 3D content in Blender, always start by checking if integrations are available:
    
        0. Before anything, always check the scene from get_scene_info()
        1. First use the following tools to verify if the following integrations are enabled:
            1. PolyHaven
                Use get_polyhaven_status() to verify its status
                If PolyHaven is enabled:
                - For objects/models: Use download_polyhaven_asset() with asset_type="models"
                - For materials/textures: Use download_polyhaven_asset() with asset_type="textures"
                - For environment lighting: Use download_polyhaven_asset() with asset_type="hdris"
            2. Sketchfab
                Sketchfab is good at Realistic models, and has a wider variety of models than PolyHaven.
                Use get_sketchfab_status() to verify its status
                If Sketchfab is enabled:
                - For objects/models: First search using search_sketchfab_models() with your query
                - Then download specific models using download_sketchfab_model() with the UID
                - Note that only downloadable models can be accessed, and API key must be properly configured
                - Sketchfab has a wider variety of models than PolyHaven, especially for specific subjects
            3. Hyper3D(Rodin)
                Hyper3D Rodin is good at generating 3D models for single item.
                So don't try to:
                1. Generate the whole scene with one shot
                2. Generate ground using Hyper3D
                3. Generate parts of the items separately and put them together afterwards
    
                Use get_hyper3d_status() to verify its status
                If Hyper3D is enabled:
                - For objects/models, do the following steps:
                    1. Create the model generation task
                        - Use generate_hyper3d_model_via_images() if image(s) is/are given
                        - Use generate_hyper3d_model_via_text() if generating 3D asset using text prompt
                        If key type is free_trial and insufficient balance error returned, tell the user that the free trial key can only generated limited models everyday, they can choose to:
                        - Wait for another day and try again
                        - Go to hyper3d.ai to find out how to get their own API key
                        - Go to fal.ai to get their own private API key
                    2. Poll the status
                        - Use poll_rodin_job_status() to check if the generation task has completed or failed
                    3. Import the asset
                        - Use import_generated_asset() to import the generated GLB model the asset
                    4. After importing the asset, ALWAYS check the world_bounding_box of the imported mesh, and adjust the mesh's location and size
                        Adjust the imported mesh's location, scale, rotation, so that the mesh is on the right spot.
    
                    You can reuse assets previous generated by running python code to duplicate the object, without creating another generation task.
    
        3. Always check the world_bounding_box for each item so that:
            - Ensure that all objects that should not be clipping are not clipping.
            - Items have right spatial relationship.
        
        4. Recommended asset source priority:
            - For specific existing objects: First try Sketchfab, then PolyHaven
            - For generic objects/furniture: First try PolyHaven, then Sketchfab
            - For custom or unique items not available in libraries: Use Hyper3D Rodin
            - For environment lighting: Use PolyHaven HDRIs
            - For materials/textures: Use PolyHaven textures
    
        Only fall back to scripting when:
        - PolyHaven, Sketchfab, and Hyper3D are all disabled
        - A simple primitive is explicitly requested
        - No suitable asset exists in any of the libraries
        - Hyper3D Rodin failed to generate the desired asset
        - The task specifically requires a basic material/color
        """
  • The @mcp.tool() decorator registers the function as an MCP tool.
    @mcp.tool()
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 key behavioral traits: 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, whether the import is automatic or requires confirmation, or what happens on failure. This provides moderate but incomplete behavioral context.

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 action. Each sentence adds value: the first states the purpose, the second and third provide behavioral details (materials, size), and the parameter section explains inputs clearly. There is minimal waste, though the structure could be slightly more streamlined by integrating parameter details into the flow.

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, 0% schema coverage, and no output schema, the description does a fair job: it covers the purpose, key behaviors, and parameter meanings. However, for a tool that generates and imports 3D assets (a potentially complex operation), it lacks details on error handling, output format beyond success/failure, or integration specifics with Blender. This leaves gaps in contextual understanding.

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 adds meaningful semantics: 'text_prompt' is described as 'A short description of the desired model in **English**', and 'bbox_condition' as 'Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.' This clarifies usage beyond the bare schema, though it could specify units or constraints for bbox_condition values.

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 by giving description of the desired asset, and import the asset into Blender'), identifies the resource (3D asset/model), and distinguishes it from siblings like 'generate_hyper3d_model_via_images' by specifying it works via text input rather than images. The purpose is unambiguous and well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: when you want to generate a 3D asset from a text description and import it into Blender. It implies usage vs. 'generate_hyper3d_model_via_images' by specifying text-based generation, but does not explicitly state when not to use it or name alternatives for similar tasks like downloading models from other sources (e.g., 'download_polyhaven_asset').

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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