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opslon

BlenderMCP

by opslon

generate_hunyuan3d_model

Create 3D assets with built-in materials using text descriptions or image references, then import them directly into Blender for 3D modeling workflows.

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

  • Handler function for generating Hunyuan3D models, sends a command to the Blender server to create a job.
    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)}"
Behavior2/5

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

With no annotations, the description carries full burden. It discloses that the tool generates and imports assets with materials, and returns a job_id for async processing with status updates. However, it lacks details on permissions, rate limits, error handling beyond generic failure, or what 'import into Blender' entails operationally.

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-structured with clear sections for purpose, parameters, and returns. Sentences are direct and informative, though the returns section could be more concise by combining success/error cases.

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?

For a tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description provides basic purpose and parameter semantics but lacks usage guidelines, detailed behavioral context, and output specifics beyond job_id. It's minimally adequate but has clear gaps given the complexity.

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 text_prompt is a short description in English/Chinese and input_image_url can be local/remote or None if only using text. This adds meaningful context beyond the schema's basic titles, though it doesn't cover all potential constraints like image format or prompt length.

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 tool generates a 3D asset using Hunyuan3D from text/image inputs and imports it into Blender with materials. It specifies the resource (3D asset) and action (generate and import), but doesn't explicitly differentiate from siblings like generate_hyper3d_model_via_text/images or import_generated_asset_hunyuan.

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 versus alternatives like generate_hyper3d_model_via_text/images or import_generated_asset_hunyuan. It mentions text/image inputs but doesn't specify use cases, prerequisites, or exclusions relative to sibling tools.

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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