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generate_hunyuan3d_model

Generate 3D assets from text prompts or image references and import them into Blender with built-in materials.

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries full behavioral burden and does well: it discloses asynchronous job submission via job_id, eventual DONE status meaning the model has been imported, error returns, and built-in materials. Could mention scene impact, but key behavior is covered.

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?

Well-structured with front-loaded purpose, a compact Parameters section, and a Returns section. Every sentence adds value with no filler or repetition.

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?

Provides enough context for a generation-and-import workflow: inputs, async behavior, success condition, and error handling. It does not explicitly point to poll_hunyuan_job_status for status checking, but the job_id and DONE status make the flow inferable.

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%, but the description fully compensates by defining text_prompt as an optional short English/Chinese description and input_image_url as an optional local/remote URL that accepts None. It also clarifies the relationship between the two: either text, image, or both.

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?

States a specific action, resource, and method: generate a 3D asset using Hunyuan3D from text, image, or both, and import it into Blender. This distinguishes it from sibling generation tools like generate_hyper3d_model_via_text/images and from standalone import tools.

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?

Clearly indicates when to use the tool (Hunyuan3D generation with auto-import) and how to choose inputs (text, image, or both). It does not explicitly name sibling alternatives or state when-not-to-use, but the context is clear enough for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.