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ujs204

BlenderMCP

by ujs204

generate_hyper3d_model_via_text

Create 3D models in Blender using text descriptions. Generates assets with built-in materials and imports them directly into your scene.

Instructions

Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender. The 3D asset has built-in materials. The generated model has a normalized size, so re-scaling after generation can be useful.

Parameters:

  • text_prompt: A short description of the desired model in English.

  • bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.

Returns a message indicating success or failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
text_promptYes
bbox_conditionNo

Implementation Reference

  • Handler function decorated with @mcp.tool() that registers and implements the tool. It processes the text prompt and optional bbox_condition using _process_bbox helper, sends 'create_rodin_job' command to Blender, and returns task details or error.
    @mcp.tool()
    def generate_hyper3d_model_via_text(
        ctx: Context,
        text_prompt: str,
        bbox_condition: list[float]=None
    ) -> str:
        """
        Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.
        The 3D asset has built-in materials.
        The generated model has a normalized size, so re-scaling after generation can be useful.
        
        Parameters:
        - text_prompt: A short description of the desired model in **English**.
        - bbox_condition: Optional. If given, it has to be a list of floats of length 3. Controls the ratio between [Length, Width, Height] of the model.
    
        Returns a message indicating success or failure.
        """
        try:
            blender = get_blender_connection()
            result = blender.send_command("create_rodin_job", {
                "text_prompt": text_prompt,
                "images": None,
                "bbox_condition": _process_bbox(bbox_condition),
            })
            succeed = result.get("submit_time", False)
            if succeed:
                return json.dumps({
                    "task_uuid": result["uuid"],
                    "subscription_key": result["jobs"]["subscription_key"],
                })
            else:
                return json.dumps(result)
        except Exception as e:
            logger.error(f"Error generating Hyper3D task: {str(e)}")
            return f"Error generating Hyper3D task: {str(e)}"
  • Helper function used by the tool to normalize the bbox_condition into a list of integers scaled to 0-100 range based on the maximum value.
    def _process_bbox(original_bbox: list[float] | list[int] | None) -> list[int] | None:
        if original_bbox is None:
            return None
        if all(isinstance(i, int) for i in original_bbox):
            return original_bbox
        if any(i<=0 for i in original_bbox):
            raise ValueError("Incorrect number range: bbox must be bigger than zero!")
        return [int(float(i) / max(original_bbox) * 100) for i in original_bbox] if original_bbox else None
  • The @mcp.tool() decorator registers the generate_hyper3d_model_via_text function as an MCP tool.
    @mcp.tool()
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the 3D asset has built-in materials, the model has normalized size requiring potential rescaling, and it returns a success/failure message. However, it misses details like rate limits, authentication needs, or what 'import into Blender' entails operationally. No contradiction with annotations exists.

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?

The description is appropriately sized and front-loaded, starting with the core purpose. Sentences like 'The 3D asset has built-in materials' and 'The generated model has a normalized size' add value without redundancy. The parameter section is clear but could be more integrated. Overall, it's efficient with minimal waste.

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?

Given no annotations, 0% schema coverage, and no output schema, the description is moderately complete. It covers the tool's purpose, key behaviors, and parameter semantics, but lacks details on error handling, Blender integration specifics, or output beyond success/failure. For a generative tool with two parameters, it's adequate but has gaps in operational context.

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 adds meaningful semantics: 'text_prompt' is described as 'A short description of the desired model in **English**,' and 'bbox_condition' as 'Optional... Controls the ratio between [Length, Width, Height] of the model.' This clarifies purpose and constraints beyond the bare schema, though it could detail format expectations more.

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's purpose: 'Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.' It specifies the verb (generate and import), resource (3D asset), and technology (Hyper3D). However, it doesn't explicitly differentiate from its sibling 'generate_hyper3d_model_via_images' beyond the input method, missing a clear distinction about when to use text vs. image prompts.

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?

The description implies usage by mentioning the tool generates from a text description and imports to Blender, suggesting it's for creating new 3D assets. However, it lacks explicit guidance on when to use this tool versus alternatives like 'generate_hyper3d_model_via_images' or other asset download tools. No exclusions or prerequisites are stated.

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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