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prepare_ai_model_for_print

Detects and fixes unit mismatches in AI-generated 3D models, scales them to printable size, and provides recommendations for mesh simplification and hollowing to ensure successful prints.

Instructions

Prepare any AI-generated model for printing — auto-fixes the unit problem.

        AI model generators (Meshy, Tripo, Stability, Gemini) routinely
        export models in meters instead of millimeters, producing figurines
        that are 1.9mm tall.  This tool detects and fixes that, plus runs
        the full validation pipeline and provides smart recommendations
        for simplification and hollowing.

        Pipeline:
            1. Run validate_and_prepare for baseline analysis
            2. Auto-scale detection — if max dim < 10mm, scale to a
               reasonable size (or to target_height_mm if provided)
            3. Mesh simplification recommendation (if > 100K triangles)
            4. Smart hollow recommendation (only when appropriate)
            5. Re-validate the scaled model
            6. Return combined before/after comparison

        Works with STL, OBJ, and 3MF files.

        :param input_path: Path to the AI-generated model file.
        :param target_height_mm: Desired height in mm.  If 0, auto-detects
            a reasonable size based on model aspect ratio.
        :param printer_id: Optional printer model ID for bed-fit checking.
        :param material: Material name (default "PLA") for material checks.
        :returns: Dict with original/prepared comparison, actions taken,
            recommendations, and next_action for slicing.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
materialNoPLA
input_pathYes
printer_idNo
target_height_mmNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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 details the six-step pipeline, including auto-scaling, simplification, hollowing, and re-validation. However, it does not clarify whether the tool modifies the input file in place or creates a new file, which is a behavioral gap.

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 with a clear header, bulleted pipeline, and parameter list. It is front-loaded with the core purpose. While somewhat lengthy, every sentence adds value and the structure aids readability.

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 the tool's complexity, the description covers the pipeline, parameters, output structure, and supported file types. It does not mention error handling or prerequisites, but overall it provides sufficient context for an agent to understand and invoke the tool 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?

Schema description coverage is 0%, but the tool's description provides detailed semantics for all four parameters: input_path, target_height_mm (with behavior for 0), printer_id, and material (with default). It fully compensates for the missing schema descriptions.

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 identifies the tool's purpose: preparing AI-generated models for printing by auto-fixing the unit issue. It specifies the resource (AI-generated models) and the action (prepare for printing), and distinguishes from sibling tools like validate_and_prepare by including auto-scaling and smart recommendations.

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 explicitly states the tool is for AI-generated models from specific generators (Meshy, Tripo, etc.) that have unit problems, providing clear context for when to use. It does not explicitly state when not to use or list alternatives, but the context is strong.

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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