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cache_model

Copy a 3D model file into the local cache for reuse, automatically detecting duplicates via SHA-256 hash and storing metadata like source, prompt, tags, and dimensions.

Instructions

Add a 3D model file to the local cache for reuse across jobs.

        Copies the file into ``~/.kiln/model_cache/`` and stores metadata
        (source, prompt, tags, dimensions) in the database.  Duplicate files
        are detected automatically by SHA-256 hash.

        Args:
            file_path: Path to the model file on disk.
            source: Origin — ``"thingiverse"``, ``"myminifactory"``, ``"meshy"``,
                ``"openscad"``, ``"upload"``, etc.
            source_id: Marketplace thing ID or generation job ID.
            prompt: For generated models, the text prompt used.
            tags: Comma-separated tags (e.g. ``"benchy,calibration,test"``).
            dimensions: JSON object with bounding box in mm, e.g.
                ``'{"x": 60, "y": 31, "z": 48}'``.
            metadata: Optional JSON object with extra data.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
promptNo
sourceYes
metadataNo
file_pathYes
source_idNo
dimensionsNo
Behavior4/5

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

The description discloses key behaviors: file copying to '~/.kiln/model_cache/', metadata storage in a database, and duplicate detection via SHA-256. This goes beyond the schema, especially since no annotations are provided. It clearly indicates a write operation and explains the caching mechanism.

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 concise summary followed by parameter details in an 'Args:' block. It is front-loaded with the purpose. A slight reduction in parameter detail (e.g., more compact format) could improve conciseness, but it is already efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 parameters, 2 required, no output schema, and zero schema description coverage, the description provides complete coverage: explains all parameters, gives examples, describes the caching process and duplicate detection. It is self-sufficient for an agent to invoke 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%, so the description carries the full burden. It provides detailed explanations for all 7 parameters, including examples for 'source' (enum-like list), 'tags' (comma-separated example), and 'dimensions' (JSON object with bounding box format). This adds significant meaning beyond the schema's type-only fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it adds a 3D model to a local cache for reuse across jobs, with specific details about storage location and metadata. While the verb and resource are specific, it does not explicitly distinguish from sibling tools like 'cache_design' or 'get_cached_model', leaving some ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does but provides no guidance on when to use it versus alternatives like 'cache_design' or 'delete_cached_model'. There is no mention of prerequisites, when not to use, or when to prefer another tool.

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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