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generate_hyper3d_model_via_images

Create 3D models with built-in materials from images and import them directly into Blender for 3D scene integration.

Instructions

Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated 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:

  • input_image_paths: The absolute paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.

  • input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.

  • bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.

Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode. Returns a message indicating success or failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_image_pathsNo
input_image_urlsNo
bbox_conditionNo

Implementation Reference

  • The core handler function for the 'generate_hyper3d_model_via_images' tool, decorated with @mcp.tool(). Validates input images from paths or URLs, encodes local images to base64, processes bounding box condition, sends 'create_rodin_job' command to Blender via socket, and returns JSON with task_uuid and subscription_key on success or error details.
    @mcp.tool()
    def generate_hyper3d_model_via_images(
        ctx: Context,
        input_image_paths: list[str]=None,
        input_image_urls: list[str]=None,
        bbox_condition: list[float]=None
    ) -> str:
        """
        Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated 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:
        - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.
        - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.
        - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.
    
        Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.
        Returns a message indicating success or failure.
        """
        if input_image_paths is not None and input_image_urls is not None:
            return f"Error: Conflict parameters given!"
        if input_image_paths is None and input_image_urls is None:
            return f"Error: No image given!"
        if input_image_paths is not None:
            if not all(os.path.exists(i) for i in input_image_paths):
                return "Error: not all image paths are valid!"
            images = []
            for path in input_image_paths:
                with open(path, "rb") as f:
                    images.append(
                        (Path(path).suffix, base64.b64encode(f.read()).decode("ascii"))
                    )
        elif input_image_urls is not None:
            if not all(urlparse(i) for i in input_image_paths):
                return "Error: not all image URLs are valid!"
            images = input_image_urls.copy()
        try:
            blender = get_blender_connection()
            result = blender.send_command("create_rodin_job", {
                "text_prompt": None,
                "images": images,
                "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)}"
  • Supporting helper function used by the tool to 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
  • MCP prompt that provides usage instructions for the tool, including when to use generate_hyper3d_model_via_images in the asset creation workflow.
    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
        """
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the generated asset has built-in materials, has a normalized size (requiring re-scaling), and returns a success/failure message. However, it lacks details on permissions, rate limits, error conditions, or what 'import into Blender' entails (e.g., file location, overwrite behavior). For a tool with no annotations, this is a moderate disclosure but misses critical operational 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 well-structured and appropriately sized. It starts with the core purpose, adds behavioral details, then lists parameters with clear explanations. Every sentence adds value, but it could be slightly more front-loaded by integrating parameter constraints earlier. No wasted words, but minor structural improvements are possible.

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 complexity (3 parameters, no annotations, no output schema), the description is moderately complete. It covers purpose, key behaviors, and parameter semantics well, but lacks output details beyond a generic success/failure message, and doesn't address error handling or integration specifics with Blender. For a tool with no structured support, it should provide more operational context to be fully helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all three parameters: explains that 'input_image_paths' requires absolute paths and is for MAIN_SITE mode, 'input_image_urls' is for FAL_AI mode, 'bbox_condition' controls length/width/height ratio as a list of 3 ints, and clarifies that only one of the image parameters should be used based on mode. This adds significant meaning beyond the bare schema.

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 images of the wanted asset, and import the generated asset into Blender.' It specifies the verb (generate), resource (3D asset), and method (via images), and distinguishes it from its sibling 'generate_hyper3d_model_via_text' by mentioning images. However, it doesn't explicitly differentiate from other 3D-related tools like 'download_polyhaven_asset' or 'download_sketchfab_model' beyond the generation method.

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 have images of a wanted asset and want to generate a 3D model with Hyper3D, importing it into Blender. It mentions the sibling 'generate_hyper3d_model_via_text' implicitly by specifying 'via images,' but doesn't explicitly state when to choose this over text-based generation or other 3D download tools. No explicit exclusions or alternatives are named.

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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