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generate_hunyuan3d_model

Create 3D assets with built-in materials for Blender using text descriptions or image references via Hunyuan3D AI generation.

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
input_image_urlNo

Implementation Reference

  • The implementation of the generate_hunyuan3d_model tool handler.
    def generate_hunyuan3d_model(
        ctx: Context,
        text_prompt: str = None,
        input_image_url: str = None
    ) -> str:
        """
        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
        """
        try:
            blender = get_blender_connection()
            result = blender.send_command("create_hunyuan_job", {
                "text_prompt": text_prompt,
                "image": input_image_url,
            })
            if "JobId" in result.get("Response", {}):
                job_id = result["Response"]["JobId"]
                formatted_job_id = f"job_{job_id}"
                return json.dumps({
                    "job_id": formatted_job_id,
                })
            return json.dumps(result)
        except Exception as e:
            logger.error(f"Error generating Hunyuan3D task: {str(e)}")
            return f"Error generating Hunyuan3D task: {str(e)}"
Behavior4/5

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

With no annotations, the description carries the full burden and discloses key behaviors: async job flow (job_id, 'DONE' status), side effects ('import the asset into Blender'), and output characteristics ('built-in materials'). It does not cover rate limits or auth requirements.

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?

Well-organized with clear sections (purpose, parameters, returns). Front-loaded with the core action. The returns section is slightly verbose but necessary given the lack of output schema.

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 zero annotations, no output schema, and simple input types, the description adequately covers the async return pattern, import destination, and material properties. It could mention the existence of polling tools (poll_hunyuan_job_status) for status checking.

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?

With 0% schema description coverage, the description fully compensates by documenting both parameters in detail: text_prompt accepts 'English/Chinese' descriptions and input_image_url accepts 'local or remote url', including the 'Accepts None' constraint.

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 uses specific verbs ('Generate', 'import') and identifies the specific technology (Hunyuan3D) and target (Blender), distinguishing it from Hyper3D siblings. However, it lacks explicit comparison or selection criteria between Hunyuan3D and Hyper3D alternatives.

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?

It clearly explains input combinations ('either text description, image reference, or both') but provides no explicit guidance on when to use this tool versus the Hyper3D generators or other asset sources like Polyhaven/Sketchfab.

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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