Skip to main content
Glama

import_generated_asset

Import the 3D asset generated by Hyper3D Rodin into Blender after the generation task finishes. Specify 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
Behavior4/5

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

No annotations exist, so the description carries the full burden. It discloses the expected return ('Return if the asset has been imported successfully') and a prerequisite (completion of generation). It does not detail potential side effects or error conditions, but for a simple import tool the key behavior is adequately disclosed.

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 concise and front-loaded with the purpose. Parameter details are presented in a clean bulleted list with no redundant information. Every sentence adds value, and the return behavior is communicated in a single short sentence.

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?

Given the tool's moderate complexity, the description covers purpose, parameters, usage prerequisites, and return value. It does not mention error handling if both task_uuid and request_id are provided or neither, which is a minor gap, but overall it is sufficient for correct invocation.

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 coverage is 0%, so the description is essential. It explains all three parameters: `name` (object in scene), `task_uuid` (for MAIN_SITE mode), and `request_id` (for FAL_AI mode), including the exclusivity constraint. This fully compensates for the lack of schema 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 clearly states a specific verb ('Import') and a specific resource ('the asset generated by Hyper3D Rodin'). It distinguishes from the sibling tool `import_generated_asset_hunyuan` by explicitly naming the source system, and the purpose is unique among 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 states when to use the tool ('after the generation task is completed') and provides a critical usage rule ('Only give one of {task_uuid, request_id} based on the Hyper3D Rodin Mode'). While it does not explicitly contrast with alternative tools, the mode-specific guidance is clear and actionable.

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/jukaben32/Bender-MCP'

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