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opslon

BlenderMCP

by opslon

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 tool handler for "generate_hyper3d_model_via_images" which takes image paths/URLs and sends a command to Blender to create a 3D generation job.
    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)}"
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 that the tool generates a 3D asset with built-in materials, imports it into Blender, and normalizes size. It mentions success/failure returns but doesn't cover behavioral traits like required permissions, rate limits, error handling, or what 'import into Blender' entails operationally. The description adds some context but leaves gaps for a mutation tool.

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: the first sentence states the core purpose, followed by key details (materials, scaling), then a structured parameter section. Every sentence adds value, but the parameter explanations could be more integrated (e.g., merging mode dependencies into the purpose). 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, 0% schema coverage, no output schema, no annotations), the description is moderately complete. It covers purpose, parameters, and basic behavior but lacks details on output (only 'success or failure' message), error cases, integration with Blender, or dependencies on other tools (e.g., status checks). For a tool with multiple parameters and no structured support, it should do more to guide usage.

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 adds significant meaning: it explains that input_image_paths requires absolute paths and is for MAIN_SITE mode, input_image_urls is for FAL_AI mode, both must be lists, and bbox_condition controls [Length, Width, Height] ratio as a list of 3 ints. This clarifies usage beyond the bare schema, though it doesn't detail format constraints (e.g., image types) or default behaviors.

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), method (via images), and destination (Blender). However, it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_text', which uses text input 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage: it specifies that only one of {input_image_paths, input_image_urls} should be given depending on Hyper3D Rodin's current mode (MAIN_SITE vs FAL_AI). It also mentions re-scaling after generation can be useful. However, it doesn't explicitly state when to use this tool versus alternatives like 'generate_hunyuan3d_model' or 'import_generated_asset', nor does it 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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