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auto_orient_model

Evaluates multiple 3D model rotations to find the optimal print orientation, improving bed adhesion and reducing support needs. Optionally applies the best orientation and saves a reoriented STL file.

Instructions

Find the optimal print orientation for a 3D model.

        Evaluates multiple rotations of the model and scores each based
        on bed adhesion, support requirements, print height, and
        overhang coverage.  Optionally applies the best orientation and
        writes a reoriented STL file.

        Args:
            file_path: Path to an STL or OBJ mesh file.
            candidates: Number of candidate orientations to evaluate
                (default 24).
            nozzle_diameter: Printer nozzle diameter in mm (default 0.4).
            apply: If True, apply the best orientation and write the
                reoriented STL to disk.
            output_path: Output path for the reoriented STL.  Only used
                when ``apply`` is True.  Defaults to
                ``<input>_oriented.stl``.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
applyNo
file_pathYes
candidatesNo
output_pathNo
nozzle_diameterNo
Behavior3/5

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

The description discloses key behaviors (evaluation, scoring, optional write) but lacks details on side effects (e.g., does writing a new STL overwrite the original? Permissions needed?). Without annotations, more explicit behavioral context would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear first sentence followed by bullet-like parameter explanations. It is concise, with no redundant or irrelevant information. Every sentence serves a purpose.

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?

The description covers the main functionality and parameters, but lacks detail on return values (no output schema) and scoring criteria. For a tool with moderate complexity and no annotations, more completeness on output behavior would be beneficial.

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 coverage is 0%, so the description must explain parameters. It does so for all five: file_path, candidates, nozzle_diameter, apply, output_path, including defaults and conditional use. This adds significant meaning beyond the bare 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 the tool's purpose ('Find the optimal print orientation for a 3D model') and details how it works (evaluates rotations, scores based on adhesion, supports, etc.). It distinguishes itself from siblings like 'optimize_print_orientation' by specifying the evaluation criteria and optional file writing.

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 implies usage (when needing optimal orientation) but does not explicitly state when not to use or mention alternatives. Given sibling tools like 'optimize_print_orientation' and 'analyze_model_geometry', more explicit guidance would help.

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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