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Eminemminem

BlenderMCP

by Eminemminem

generate_hyper3d_model_via_images

Create 3D models with built-in materials from images and import them into Blender for 3D asset generation.

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' MCP tool. It validates input images (either paths or URLs), encodes path-based images to base64, processes bbox_condition using helper, and sends a 'create_rodin_job' command to Blender addon via socket. The @mcp.tool() decorator registers the tool and infers schema from signature/docstring.
    @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 bbox_condition into percentages (0-100) based on max dimension, handling float/int inputs.
    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
  • Utility tool to check Hyper3D status, prerequisite for using the generation tools.
    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)}"
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 and does well by disclosing key behaviors: it generates a 3D asset with built-in materials, imports it into Blender, normalizes the model size (suggesting re-scaling may be needed), and returns a success/failure message. It also mentions mode dependencies (MAIN_SITE vs. FAL_AI) and parameter exclusivity. However, it lacks details on permissions, rate limits, or error handling specifics.

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 with the core purpose. Each sentence adds value: purpose, material info, scaling note, and parameter details. However, the parameter section could be slightly more streamlined, and there's minor redundancy in explaining list wrapping for single images.

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?

Given the complexity (3 parameters, 0% schema coverage, no annotations, no output schema), the description is quite complete. It covers purpose, behaviors, and all parameters with semantics. The main gap is the lack of output details beyond 'success or failure message', but since there's no output schema, this is a minor shortfall. It adequately compensates for the missing structured data.

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, which it does effectively. It explains all three parameters: 'input_image_paths' (absolute paths for MAIN_SITE mode), 'input_image_urls' (URLs for FAL_AI mode), and 'bbox_condition' (optional list of 3 ints for ratio control). It adds critical context like wrapping single images into lists, parameter exclusivity, and mode dependencies, going well 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 and import), resource (3D asset), and method (via images). However, it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_text', which uses text instead of 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 implies usage by specifying that it requires images as input, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'generate_hyper3d_model_via_text' or 'import_generated_asset'. It mentions dependencies on Hyper3D Rodin modes (MAIN_SITE vs. FAL_AI), which offers some contextual hints but not clear when/when-not directives.

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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