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generate_model_from_image

Generate a 3D model from a reference image. The agent analyzes the image to write OpenSCAD code, or uses the Meshy API for AI mesh reconstruction when configured.

Instructions

Make a 3D model from a reference image.

        DEFAULT (keyless): you (the agent) can SEE the image — study it
        and write the OpenSCAD yourself, then compile_scad. You do NOT
        need an image-to-3D provider, and with no key configured this
        tool hands the job back to your vision instead of erroring.

        OPT-IN cloud path: when the user has set their OWN
        KILN_MESHY_API_KEY, this submits the image to Meshy for an AI mesh
        reconstruction. The image should show the object clearly against a
        clean background for best results.

        **EXPERIMENTAL:** AI-generated models are experimental.  Always
        validate the mesh before printing.

        **Image tips:**
        - Use a clear, well-lit photo of the object.
        - Plain/solid backgrounds produce better results.
        - Show the full object — avoid cropped or partial views.
        - Multiple angles are not supported; use the best single view.

        Args:
            image_url: URL to the reference image (PNG, JPG).  Must be
                publicly accessible.
            provider: Generation provider.  Currently only ``"meshy"``
                supports image-to-3D.
            style: Optional style hint (``"realistic"`` or ``"sculpture"``).
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNo
providerNomeshy
image_urlYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the dual-mode behavior, experimental nature, and image requirements. However, it does not specify the return value or output format, nor does it mention potential costs with the API key or error handling for invalid images.

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 front-loaded with the core purpose, then logically breaks into default/opt-in, experimental note, image tips, and parameter details. While somewhat lengthy, each section contributes necessary context without irrelevant detail.

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 the tool's complexity (dual-mode, external API), the description covers usage scenarios and parameter guidance well. However, it lacks explanation of the output (what is returned to the agent), error scenarios, and lifecycle of the generated model, which are important for an agent to use it correctly.

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?

The description adds significant meaning beyond the bare input schema: it defines image_url as requiring a publicly accessible URL (PNG/JPG), limits provider to 'meshy', and enumerates style options ('realistic' or 'sculpture'). With 0% schema description coverage, this fully compensates.

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 it makes a 3D model from a reference image and distinguishes between a default mode (agent uses vision to write OpenSCAD) and an opt-in cloud path (using Meshy). This differentiation sets it apart from sibling tools like generate_model which likely take text prompts.

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?

The description explains when to use the keyless default path vs the opt-in cloud path, and provides image tips for best results. However, it does not explicitly compare with sibling tools or state when not to use this tool, leaving some ambiguity.

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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