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mgi24

Blender MCP (Fork)

by mgi24

generate_hyper3d_model_via_text

Use a text prompt to generate a 3D model with built-in materials, import it into Blender, and optionally set its dimensions via a bounding box.

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
user_promptNo
bbox_conditionNo
Behavior3/5

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

The description discloses that the asset has built-in materials and normalized size, and that it imports into Blender. However, it does not mention side effects like whether it replaces existing objects or requires permissions. With no annotations, this partial transparency leaves some ambiguity.

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 well-structured: two sentences of overview followed by a bulleted parameter list. It avoids redundancy and is easy to parse.

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 output schema, the description does not need to explain return values, but it omits important context such as expected side effects on the Blender scene (e.g., whether it adds a new object) and the meaning of user_prompt. This makes it adequate but not fully complete.

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?

The description explains text_prompt and bbox_condition (e.g., 'Controls the ratio between [Length, Width, Height]'), adding value beyond the schema. However, it omits the third parameter (user_prompt) entirely, so coverage is incomplete.

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 the tool's purpose: 'Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.' It distinguishes from siblings like generate_hunyuan3d_model by explicitly mentioning Hyper3D and the dual generation+import action.

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?

The description does not specify when to use this tool versus alternatives (e.g., generate_hunyuan3d_model). It only hints at usage with 're-scaling after generation can be useful,' but lacks explicit guidance on selection criteria.

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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