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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()

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.0
    • addedInput schema / title
      Added value: +"generate_hyper3d_model_via_textArguments"
  2. First observed

TDQS

A4.2/5.0
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 of behavioral disclosure. It does well by disclosing that the tool imports the asset into Blender, that materials are built-in, that the model size is normalized and may need rescaling, and that it returns a success/failure message. It does not mention whether generation is asynchronous, whether it blocks, or how the imported object can be referenced afterward, but it provides substantial useful behavior context.

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 compact and well-structured: purpose and side effect first, then relevant model characteristics, then parameter details, then return value. Every sentence adds information, and there is no redundant restatement of schema fields or filler.

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

Completeness4/5

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

For a two-parameter tool with no annotations and no output schema, the description is nearly complete. It covers input semantics, generated asset characteristics, the Blender import side effect, and the return message. Minor gaps are lack of clarification about asynchronous behavior and how the agent can locate or reference the imported asset after success.

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's parameter notes are essential. It explains that text_prompt must be a short English description, and that bbox_condition is optional, must be a list of floats of length 3, and controls the Length/Width/Height ratio. This adds meaningful semantic detail beyond the raw schema, though ranges or a more precise ratio explanation would strengthen it further.

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 states a specific verb ('Generate'), the specific service (Hyper3D), the input mode (description/text), and the side effect (import into Blender). It also adds useful purpose details: built-in materials and normalized size. This clearly distinguishes it from image-based generation like generate_hyper3d_model_via_images, even though that sibling is not named.

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 when to use the tool: when you have a textual description of a 3D asset and want it generated and imported into Blender. However, it does not explicitly contrast with generate_hyper3d_model_via_images, import_generated_asset, or the other generation tools. No when-not-to-use guidance or alternative selection criteria are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.