Skip to main content
Glama

generate_hunyuan3d_model

Generate 3D assets from text or image prompts and import them into Blender with built-in materials.

Instructions

Generate 3D asset using Hunyuan3D by providing either text description, image reference, 
or both for the desired asset, and import the asset into Blender.
The 3D asset has built-in materials.

Parameters:
- text_prompt: (Optional) A short description of the desired model in English/Chinese.
- input_image_url: (Optional) The local or remote url of the input image. Accepts None if only using text prompt.

Returns: 
- When successful, returns a JSON with job_id (format: "job_xxx") indicating the task is in progress
- When the job completes, the status will change to "DONE" indicating the model has been imported
- Returns error message if the operation fails

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
text_promptNo
user_promptNo
input_image_urlNo
Behavior2/5

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

No annotations provided, so description carries full burden. It describes the async workflow (job_id, DONE status) but is ambiguous about whether import is automatic or requires a separate tool (import_generated_asset_hunyuan exists as sibling). Missing details on failure modes and rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Reasonably front-loaded with purpose; separate parameter and return sections. However, the sentence 'The 3D asset has built-in materials.' adds marginal value and could be integrated. No wasted words beyond that.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and no annotations, the description leaves significant gaps: it doesn't mention that user_prompt exists, doesn't explain relationship with sibling tools (poll_hunyuan_job_status, import_generated_asset_hunyuan), and doesn't clarify if import is automatic or manual.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It describes text_prompt and input_image_url but omits user_prompt entirely, which is a parameter in the schema. Descriptions are brief and lack formatting 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?

Description clearly states it generates a 3D asset via Hunyuan3D from text and/or image and imports into Blender. However, it doesn't differentiate from sibling Hunyuan vs Hyper3D tools, and the import timing is slightly ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives (e.g., Hyper3D tools) or on prerequisites like choosing text vs image. Does not explain that the job is asynchronous or that polling may be needed.

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/DhautarChor/blender-mcp'

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