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import_generated_asset

Import AI-generated 3D models from Hyper3D Rodin into Blender scenes using task UUIDs or request IDs to complete your modeling workflow.

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

  • MCP tool handler implementation for 'import_generated_asset'. It connects to Blender, prepares parameters (name, task_uuid or request_id), sends a command to import the generated asset, and returns the result or error.
    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)}"
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 imports an asset and returns success status, which is basic behavioral info. However, it lacks details on permissions, error handling, or side effects (e.g., whether it modifies existing assets). It doesn't contradict annotations, as there are none.

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 with three sentences: purpose, parameters, and return. It's front-loaded with the main action. However, the parameter list could be more integrated into prose, and the 'Only give one...' instruction is slightly redundant with the mode context.

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 3 parameters with 0% schema coverage, no annotations, and no output schema, the description provides basic purpose and parameter guidance but lacks details on return values (beyond success status), error cases, or prerequisites. It's minimally adequate but has gaps for a tool with multiple parameters and no structured support.

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 explains that 'name' is the object name in the scene, 'task_uuid' is for MAIN_SITE mode from generate model step, and 'request_id' is for FAL_AI mode from generate model step. This adds crucial meaning beyond the schema's bare parameter names and types, though it could specify format or constraints.

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 action ('Import') and resource ('asset generated by Hyper3D Rodin'), specifying it occurs after generation task completion. It distinguishes from siblings like 'generate_hyper3d_model_via_images/text' by focusing on import rather than generation, though it doesn't explicitly contrast with all siblings.

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 provides clear context for when to use this tool: after a Hyper3D Rodin generation task is completed. It distinguishes between modes (MAIN_SITE vs. FAL_AI) and specifies to use only one of task_uuid or request_id based on mode. However, it doesn't explicitly state when not to use it or compare with alternatives like 'poll_rodin_job_status'.

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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