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generate_hyper3d_model_via_images

Create 3D models with materials from images using Hyper3D and import them into Blender for 3D design workflows.

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. It validates input images (paths or URLs), encodes local images to base64, normalizes the bbox_condition, and submits a job to Blender's Hyper3D Rodin integration via 'create_rodin_job' command. Returns task details on success or error info.
    @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 and scale the bbox_condition parameter to a list of integers between 0 and 100 based on the maximum value.
    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
  • Related helper tool to check the status of Hyper3D Rodin integration in Blender, which is prerequisite for using the generate_hyper3d_model_via_images tool.
    @mcp.tool()
    def get_hyper3d_status(ctx: Context) -> str:
        """
        Check if Hyper3D Rodin integration is enabled in Blender.
        Returns a message indicating whether Hyper3D Rodin features are available.
    
        Don't emphasize the key type in the returned message, but sliently remember it. 
        """
        try:
            blender = get_blender_connection()
            result = blender.send_command("get_hyper3d_status")
            enabled = result.get("enabled", False)
            message = result.get("message", "")
            if enabled:
                message += ""
            return message
        except Exception as e:
            logger.error(f"Error checking Hyper3D status: {str(e)}")
            return f"Error checking Hyper3D status: {str(e)}"
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 of behavioral disclosure. It adds some context: the tool imports the asset into Blender, includes built-in materials, and returns a success/failure message. However, it doesn't cover critical aspects like whether this is a read-only or destructive operation, potential rate limits, authentication needs, or what happens if the generation fails. The description doesn't contradict annotations (none exist), but it's incomplete for a tool that likely involves complex processing.

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 purpose. It uses bullet points for parameters, which aids readability. However, some sentences could be more concise (e.g., 'Even if only one image is provided, wrap it into a list' is repeated), and the structure mixes general info with parameter details without clear sectioning.

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 the complexity (3 parameters, 0% schema coverage, no output schema, no annotations), the description is moderately complete. It covers the purpose, parameter semantics, and some behavioral context (import to Blender, materials, size). However, it lacks details on output format beyond success/failure, error handling, dependencies on other tools (e.g., 'poll_rodin_job_status'), and integration with sibling tools, leaving gaps for effective agent use.

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 provides meaningful semantics for all three parameters: 'input_image_paths' and 'input_image_urls' are explained with mode dependencies and list-wrapping requirements, and 'bbox_condition' is described as controlling the model's length-width-height ratio with a specific format. This adds significant value beyond the bare schema, though it doesn't detail exact formats or constraints for the bbox condition.

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 technology ('Hyper3D'), and distinguishes it from sibling tools like 'generate_hyper3d_model_via_text' by using images instead of text. However, it doesn't explicitly differentiate from other 3D-related tools like 'import_generated_asset' or 'download_polyhaven_asset' in terms of when to choose this over those.

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 context by mentioning Hyper3D Rodin modes (MAIN_SITE vs. FAL_AI) and the requirement to use either 'input_image_paths' or 'input_image_urls' based on mode. It also notes that the generated model has normalized size, suggesting re-scaling after generation. However, it lacks explicit guidance on when to use this tool versus alternatives like 'generate_hyper3d_model_via_text' or 'import_generated_asset', and doesn't 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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