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prepare_ai_model_for_print

Automatically detect and fix unit scaling errors in AI-generated 3D models, validate for print readiness, and provide recommendations for simplification and hollowing.

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
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 pipeline (validation, auto-scaling, simplification recommendation, hollow recommendation, re-validation) and auto-scaling condition (if max dim < 10mm). It mentions supported file formats. However, it does not explicitly state whether the original file is modified or preserved, and the 'smart hollow recommendation' criteria remain vague. Slight gap in non-destructive behavior clarity.

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 opening sentence, a bulleted pipeline list, and parameter docblock. It is slightly verbose but each part adds value. The information is front-loaded and logically organized, making it easy for an AI agent to parse quickly.

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 has 4 parameters, no output schema, and no annotations, the description provides adequate context. It explains the pipeline, parameter semantics, supported file types, and high-level return value (comparison dict). However, it does not specify the exact keys or types in the return dict, and could clarify handling of optional parameters and edge cases (e.g., when input file is not AI-generated). Nonetheless, it covers the essential information for typical use.

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 description includes a comprehensive docstring for all four parameters. It explains the meaning of default values (e.g., target_height_mm=0 triggers auto-detection, material default 'PLA') and optional nature of printer_id. This adds substantial value beyond the bare schema, fully compensating for the lack of parameter descriptions in the schema.

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 prepares AI-generated models for printing and auto-fixes the unit problem (meters to millimeters). It distinguishes itself by naming specific AI generators (Meshy, Tripo, Stability, Gemini) and outlining a multi-step pipeline. The verb 'prepare' combined with 'for print' makes the tool's purpose unambiguous.

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 provides clear context on when to use this tool (AI-generated models with unit issues) and outlines the pipeline steps. However, it does not explicitly state when not to use it or mention alternative tools (e.g., validate_and_prepare, simplify_mesh_model) for non-AI models. Despite this, the usage scenario is well-defined.

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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