Skip to main content
Glama

import_generated_asset

Import the 3D asset generated by Hyper3D Rodin into Blender after generation completes. Provide the task UUID or request ID to load the model into the scene.

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
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It adds useful context about the parameter exclusivity and the return value ('Return if the asset has been imported successfully'). However, it does not disclose potential side effects like scene mutation, name conflicts, or failure modes, so transparency is incomplete.

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 well-structured with a clear purpose line, a concise parameter list, a warning, and a return statement. Every sentence adds necessary information without redundancy or 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 3-parameter tool with no output schema, the description covers the core aspects: what it does, when to use it, parameter semantics, and return status. It lacks error-handling details but is reasonably complete for the tool's simplicity.

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 offers only types and defaults (0% coverage), but the description thoroughly explains each parameter: 'name' as the scene object name, 'task_uuid' for MAIN_SITE mode, 'request_id' for FAL_AI mode, and the rule to supply only one. This fully compensates for the schema's lack of descriptions.

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 verb and resource: 'Import the asset generated by Hyper3D Rodin after the generation task is completed.' It clearly states the tool's function and distinguishes it from the sibling tool import_generated_asset_hunyuan by specifying the Hyper3D Rodin source.

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 explicit timing context ('after the generation task is completed') and a clear selection rule for parameters: 'Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode!' This gives practical when-to-use guidance, though it does not explicitly contrast with alternative import tools.

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/shiz81463/blender-mcp'

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