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apply_decoration

Reapply perfected decorations to any 3D model using saved settings. Automatically resolves depth, mode, and style from a previously proven recipe.

Instructions

Apply a saved decoration to a new model — proven settings, one call.

        Loads a previously saved decoration and applies it to the target
        model using the exact settings that worked before.  Automatically
        resolves depth, mode, and image style from the proven recipe.

        This is the magic tool — decorations that took many iterations
        to perfect can be replayed on any model in one call.

        :param name: Decoration name or slug.
        :param model_path: Path to the target model (STL or OBJ).
        :param material: Override material (empty = use proven or detect
            from printer).
        :param face: Which face to decorate (auto, top, bottom, etc.).
        :param printer_id: Optional printer ID for material detection.
        :returns: Dict with decorated model path and settings used.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
faceNoauto
nameYes
materialNo
model_pathYes
printer_idNo
Behavior2/5

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

No annotations provided, so description must cover behavior. It mentions it loads and applies settings, is 'one call', and 'magic', but lacks disclosure of prerequisites (e.g., decoration must exist), potential side effects, error handling, or whether it is destructive. Overly positive without transparency.

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?

Well-structured with a header line, explanatory paragraph, marketing sentence, and parameter list. Front-loaded purpose. The 'magic tool' sentence could be considered slightly verbose, but overall efficient.

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 no annotations, output schema, and low schema coverage, the description covers core functionality and parameter meanings. However, it omits error conditions (e.g., missing decoration), detailed return structure beyond a dict, and prerequisites like requiring a saved decoration.

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 coverage is 0%, so description fully compensates. It provides clear, meaningful descriptions for each parameter: name as decoration slug, model_path as STL/OBJ, material override behavior, face options, and printer_id purpose. This adds significant value beyond schema types and defaults.

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 applies a saved decoration to a new model, using proven settings. It distinguishes from siblings like save_decoration (saves) and decorate_surface (likely different). The header 'Apply a saved decoration to a new model' is specific and actionable.

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 a saved decoration exists, but does not explicitly state when to use this versus alternatives like decorate_surface or save_decoration. No exclusions or when-not-to-use guidance provided.

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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