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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

  • Handler function implementing the 'generate_hyper3d_model_via_images' tool. Processes input images (from paths or URLs), encodes them if from paths, and sends a 'create_rodin_job' command to the Blender connection with image data and optional bbox_condition. Returns 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)}"
  • Helper function used by the tool to normalize and validate the bbox_condition parameter 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
  • The @mcp.tool() decorator registers this function as an MCP tool with the name 'generate_hyper3d_model_via_images' derived from the function name.
    @mcp.tool()
  • Input schema defined by function parameters with type hints and detailed docstring describing usage, requirements, and return value.
    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.
        """
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 generated model has built-in materials, normalized size (requiring potential rescaling), and returns a success/failure message. However, it misses important details like required permissions, processing time, rate limits, or what specific failure conditions might be.

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 with clear sections: purpose statement, parameter explanations, and return value. Most sentences earn their place by providing essential information. However, the first paragraph could be more front-loaded, and some phrasing ('The generated model has a normalized size') is slightly redundant with the rescaling note.

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 tool's complexity (3D generation with multiple modes), no annotations, and no output schema, the description provides adequate basics but has gaps. It covers parameters well and mentions the Blender import, but doesn't explain the Hyper3D Rodin modes further, what 'built-in materials' entail, or details about the success/failure message format.

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?

The description adds significant semantic value beyond the 0% schema coverage. It explains that input_image_paths requires absolute paths and is for MAIN_SITE mode, while input_image_urls is for FAL_AI mode, clarifies that even single images must be wrapped in lists, and describes bbox_condition as controlling the [Length, Width, Height] ratio. This fully compensates for the lack of schema descriptions.

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 destination (Blender). However, it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_text', which creates similar assets but from text input rather than images.

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 provides some usage context by mentioning Hyper3D Rodin modes (MAIN_SITE vs. FAL_AI) and the exclusive choice between input_image_paths and input_image_urls. 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 error conditions beyond success/failure messages.

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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