Skip to main content
Glama
Eminemminem

BlenderMCP

by Eminemminem

import_generated_asset

Import 3D assets generated by Hyper3D Rodin into Blender scenes using task UUID or request ID parameters to complete AI-assisted modeling workflows.

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 primary handler function for the MCP tool 'import_generated_asset'. It is registered via @mcp.tool() decorator, defines input parameters (name, task_uuid or request_id), and proxies the import request to the Blender addon via send_command, handling errors.
    @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)}"
  • Supporting documentation in the asset_creation_strategy prompt that describes the usage of import_generated_asset as the final step in the Hyper3D Rodin model generation workflow.
    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.
  • The @mcp.tool() decorator registers the import_generated_asset function as an MCP tool.
    @mcp.tool()
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the tool imports assets and returns success status, but lacks details on permissions, error handling, rate limits, or what 'import' entails (e.g., file location, overwrite behavior). The return statement is vague without output schema.

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 front-loaded with the main purpose, followed by parameter details and a rule. It's appropriately sized, though the parameter explanations could be more integrated. No wasted sentences, but structure is slightly fragmented.

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, 0% schema coverage, no annotations, and no output schema, the description provides basic purpose and parameter guidance but lacks behavioral details (e.g., side effects, error cases) and return value specifics. It's minimally adequate but has clear gaps for a mutation tool.

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, and clarifies 'task_uuid' and 'request_id' are for different Hyper3D Rodin modes, with a critical rule to provide only one. This adds significant meaning 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 action ('Import') and resource ('asset generated by Hyper3D Rodin'), specifying it's for assets after generation tasks. It doesn't explicitly differentiate from siblings like 'download_polyhaven_asset' or 'download_sketchfab_model', but the Hyper3D Rodin context provides some distinction.

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 after a generation task is completed, but doesn't specify when to use this tool versus alternatives like 'poll_rodin_job_status' or other import/download tools. The guidance on parameters ('Only give one of {task_uuid, request_id}') is operational rather than contextual.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Eminemminem/blender-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server