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generate_model

Create a 3D model from a text description. Submit the prompt, get a job ID, and retrieve the file when ready.

Instructions

Generate a 3D model from a text prompt via external AI API (Meshy/etc).

        Start here if user has no template/image — just a text description.
        For image-based generation, use ``generate_model_from_image``.
        For parametric templates (local, no AI API needed), use ``generate_from_template``.
        To also slice + upload in one step, use ``generate_and_print``.

        **EXPERIMENTAL:** AI-generated 3D models are experimental and may not
        be suitable for printing without manual review.  Generated geometry
        can have thin walls, non-manifold faces, floating islands, or
        dimensions that exceed printer build volume.  3D printers are delicate
        hardware — always validate the generated mesh before printing.

        **When possible, prefer downloading proven community models from
        marketplaces** (Thingiverse, MyMiniFactory) over generating new ones.
        Use generation for custom/unique objects only.

        Submits a generation job to the specified provider and returns a
        job ID for status tracking.  Use ``generation_status`` to poll for
        completion, then ``download_generated_model`` to retrieve the file.

        **Prompt tips for Meshy (text-to-3D AI):**
        - Describe the physical object clearly: shape, size, purpose.
        - Include material cues: "wooden", "metallic", "smooth plastic".
        - Specify printability: "solid base", "no overhangs", "flat bottom".
        - Keep prompts under 200 words for best results (max 600 chars).
        - Good example: "A phone stand with a curved cradle, flat rectangular
          base, and angled back support. Smooth plastic surface."
        - Bad example: "make me something cool" (too vague).

        **For OpenSCAD**, the prompt must be valid OpenSCAD code.  The job
        completes synchronously and the result is immediately available.

        Args:
            prompt: Text description (or OpenSCAD code for ``openscad``).
            provider: Generation backend — ``"meshy"`` (cloud AI) or
                ``"openscad"`` (local parametric).  Default: ``"meshy"``.
            format: Desired output format (``"stl"``).  Default: ``"stl"``.
            style: Optional style hint (``"realistic"`` or ``"sculpture"``
                for Meshy).  Ignored by OpenSCAD.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNo
formatNostl
promptYes
providerNomeshy
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses experimental nature, potential geometry issues, job submission workflow, and differences between Meshy and OpenSCAD. Lacks explicit mention of costs or quotas, but otherwise highly transparent.

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?

Description is well-structured with clear sections, front-loaded purpose, and logical flow. Some redundancy and extensive prompt tips could be shortened, but overall it's efficient for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema or annotations, the description covers workflow, return value (job ID), alternatives, and warnings. It provides a complete context for an agent to understand how to use the tool and what to expect.

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 has 0% description coverage, so description must compensate. It explains `prompt` with tips, `provider` with options, `format` with default, and `style` with valid hints. This adds significant value beyond the schema, though defaults and enum values are not fully enumerated.

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 'Generate a 3D model from a text prompt via external AI API', with a specific verb and resource. It distinguishes itself from siblings by listing alternatives like `generate_model_from_image` and `generate_from_template`, making the tool's unique purpose clear.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool ('Start here if user has no template/image') and directs to alternatives for other scenarios. It also advises preferring community models over generation, offering a complete usage framework.

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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