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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 MCP tool handler for 'import_generated_asset'. Decorated with @mcp.tool(), it defines the input schema via parameters (ctx, name, optional task_uuid or request_id), handles logic to send the import command to the Blender connection, and includes error handling. This is the primary implementation of the 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)}"
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that the tool imports assets and returns success status, which covers basic behavior. However, it lacks details on potential side effects (e.g., whether it modifies existing scenes), error conditions, authentication needs, or rate limits. The description doesn't contradict annotations (none exist), but it's minimally adequate for a mutation tool without rich behavioral context.

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 four sentences: purpose statement, parameter explanations, usage rule, and return value. Each sentence adds value without redundancy. It's front-loaded with the core purpose, though the parameter details could be slightly more integrated. There's minimal waste, but the structure is straightforward rather than optimized for rapid comprehension.

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 no annotations, 0% schema coverage, and no output schema, the description does well on parameters and usage but has gaps. It explains what the tool does and how to use parameters, but lacks details on behavioral traits (e.g., idempotency, errors), output format beyond success status, or integration with siblings like 'generate_hyper3d_model_via_text'. For a mutation tool with 3 parameters, it's minimally complete but could be more comprehensive.

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 compensate fully. It does so by explaining all three parameters: 'name' as 'The name of the object in scene', 'task_uuid' for MAIN_SITE mode, and 'request_id' for FAL_AI mode. It adds critical semantic context beyond the schema's basic titles, clarifying the mode-dependent usage and exclusivity rule ('Only give one of {task_uuid, request_id}'). This effectively bridges the schema gap.

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's for assets after generation completion. It distinguishes from siblings like 'download_polyhaven_asset' or 'generate_hyper3d_model_via_text' by focusing on importing rather than creating or fetching external assets. However, it doesn't explicitly contrast with all siblings (e.g., 'get_object_info' might retrieve similar data).

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: 'after the generation task is completed' and specifies which parameter to use based on Hyper3D Rodin Mode (MAIN_SITE vs FAL_AI). It explicitly states 'Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!' This offers good guidance but doesn't mention when NOT to use it versus alternatives like 'poll_rodin_job_status' or 'get_hyper3d_status' for checking generation progress.

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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