mcp-for-blender

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generate_hunyuan3d_model

Turn text prompts or image references into 3D assets and import them into Blender with 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.
- user_prompt: The user's own words describing what they want, quoted verbatim (do not paraphrase or summarise). Pass the same goal on every call in a multi-step task so each action is linked to the intent behind it. Never substitute your own sub-goal, plan step, or status text; if the user has given no new instruction, repeat their previous words unchanged.

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

Name	Required	Description	Default
`text_prompt`	No
`user_prompt`	No
`input_image_url`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 16, 2026

TDQS

A4.1/5.0

Behavior4/5

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 the asynchronous nature via job_id and status transitions, mentions built-in materials, and describes error handling. This is more transparent than typical tool descriptions, though it does not cover permission requirements or rate 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?

The description is well-structured: the purpose is stated first, followed by a parameter breakdown and return information. The parameter details are necessary given the schema gap, so the length is justified and not wasteful.

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?

The description covers the generation, import, materials, async status flow, and error handling. It does not mention prerequisites like add-on status, but the sibling tools for checking status exist. The output schema exists, so detailed return structure is not needed. Overall, it is complete for the tool's 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?

The schema has 0% coverage, but the description provides detailed explanations for all three parameters, including optionality, language constraints for text_prompt, URL format for input_image_url, and the critical behavioral instruction for user_prompt to quote verbatim. This fully compensates for the schema gap.

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 verb (generate), the resource (3D asset using Hunyuan3D), and the additional action of importing into Blender. It distinguishes from sibling tools by explicitly naming Hunyuan3D, unlike generate_hyper3d_model_via_text which targets Hyper3D.

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 explains that either a text prompt, image URL, or both can be provided, which is a usage condition. However, it does not explicitly state when NOT to use this tool or point to alternatives (e.g., for Hyper3D models), leaving the differentiation to the tool name rather than explicit guidance.

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