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ujs204

BlenderMCP

by ujs204

import_generated_asset

Import 3D assets generated by Hyper3D Rodin into Blender scenes using task UUID or request ID parameters for completed generation tasks.

Instructions

Import the asset generated by Hyper3D Rodin after the generation task is completed.

Parameters:

  • name: The name of the object in scene

  • task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.

  • request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.

Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode! Return if the asset has been imported successfully.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
task_uuidNo
request_idNo

Implementation Reference

  • The main handler function for the 'import_generated_asset' MCP tool. It constructs parameters and sends a socket command to the Blender addon to import the generated GLB asset, handling Hyper3D Rodin task UUID or request ID.
    @mcp.tool()
    def import_generated_asset(
        ctx: Context,
        name: str,
        task_uuid: str=None,
        request_id: str=None,
    ):
        """
        Import the asset generated by Hyper3D Rodin after the generation task is completed.
    
        Parameters:
        - name: The name of the object in scene
        - task_uuid: For Hyper3D Rodin mode MAIN_SITE: The task_uuid given in the generate model step.
        - request_id: For Hyper3D Rodin mode FAL_AI: The request_id given in the generate model step.
    
        Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!
        Return if the asset has been imported successfully.
        """
        try:
            blender = get_blender_connection()
            kwargs = {
                "name": name
            }
            if task_uuid:
                kwargs["task_uuid"] = task_uuid
            elif request_id:
                kwargs["request_id"] = request_id
            result = blender.send_command("import_generated_asset", kwargs)
            return result
        except Exception as e:
            logger.error(f"Error generating Hyper3D task: {str(e)}")
            return f"Error generating Hyper3D task: {str(e)}"
  • MCP prompt that provides strategy guidance, including instructions on using import_generated_asset after Hyper3D generation.
    @mcp.prompt()
    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
        """
  • The @mcp.tool() decorator registers the import_generated_asset function as an MCP tool.
    @mcp.tool()

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations, the description does the work: it states the prerequisite ('after the generation task is completed'), the mode-dependent parameter behavior, and that it returns a success/failure indicator. However, it does not disclose side effects on the scene, such as whether importing with an existing name replaces or adds an object, nor does it describe error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is tightly structured: one sentence of purpose, a bullet-style parameter list, a prominent mutual-exclusivity warning, and a return statement. Every line adds needed operational information with no filler.

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?

For a simple three-parameter tool with no output schema, it covers the prerequisite, parameter meaning, mode-based selection, and return status. Minor gaps remain: the exact return type is not specified ('Return if...' could be read as 'whether' versus 'the asset'), and behavior when neither/both ID fields are supplied is only partially covered by the instruction.

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 schema has 0% coverage for parameter descriptions, but the tool description fully documents all three parameters: name as the scene object name, task_uuid for MAIN_SITE mode, request_id for FAL_AI mode, plus the critical rule that exactly one of the two IDs should be provided based on mode.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action and target: 'Import the asset generated by Hyper3D Rodin after the generation task is completed.' This clearly distinguishes the tool from the sibling import_generated_asset_hunyuan and from polling/status tools, so an agent knows exactly what this tool is for.

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?

It gives a clear usage context: call after the generation task is completed, and choose task_uuid or request_id according to the Hyper3D Rodin mode (MAIN_SITE vs FAL_AI). It stops short of explicitly contrasting with alternative tools such as import_generated_asset_hunyuan, so it does not quite earn a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.