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ZachHandley

blender-mcp-enhanced

by ZachHandley

generate_hyper3d_model_via_images

Generate 3D models from images using Hyper3D and import them into Blender with built-in materials. Optionally control the model's length, width, and height ratios.

Instructions

Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated 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:
- input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.
- input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.
- bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.

Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode.
Returns a message indicating success or failure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bbox_conditionNo
input_image_urlsNo
input_image_pathsNo
Behavior3/5

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

With no annotations, description discloses that the asset has built-in materials and normalized size needing rescaling, and returns a success/failure message. This is moderate but lacks details on potential side effects, overwrite behavior, or resource limits.

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 main purpose first, then parameter details. Some redundancy ('wrap it into a list' repeated) but overall concise and readable.

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 no output schema or annotations, the description covers essential aspects: input parameters, mode logic, return type, and a practical note about scaling. Lacks details on generation time, supported image formats, but adequate for a tool of this complexity.

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?

Schema coverage is 0%, so description fully compensates by explaining each parameter's meaning: absolute paths vs URLs, mode dependency, mutual exclusivity, and optional bbox_condition with ratio semantics. This adds significant value beyond the schema's type/title fields.

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 generates a 3D asset from images and imports it into Blender, using specific verb and resource. It distinguishes from sibling 'generate_hyper3d_model_via_text' by specifying images as input.

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

Usage Guidelines4/5

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

Provides explicit guidance on parameter mutual exclusivity and mode-dependent requirements ('Only one of {input_image_paths, input_image_urls} should be given at a time'). However, it does not explicitly compare to alternative tools (e.g., when to use text vs images), though the distinction is implicit in the name.

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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