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lvncer

vrmcp

by lvncer

load_vrm_model

Load a VRM model file from a specified path to prepare it for AI-controlled expression changes, pose adjustments, and animation playback.

Instructions

VRMモデルファイルを読み込む

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filePathYesVRMファイル名(例: character.vrm)環境変数 VRM_MODELS_DIR からの相対パス
Behavior2/5

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

No annotations provided, so description must cover behavioral traits. It only says 'load', but does not disclose side effects (e.g., model loaded into memory, state changes), error handling, or whether it is destructive. Missing critical behavioral details.

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?

Very concise single sentence, no redundant information. However, it could include more details without losing conciseness. Front-loading is adequate.

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

Completeness2/5

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

Given no output schema, the description should explain return values or success indicators. It does not. Also missing prerequisites (file existence, format). For a load tool with many siblings, more context is needed for an agent to use it correctly.

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?

Only one parameter, filePath, with 100% schema coverage. The description adds meaning: it specifies the file name example and clarifies the path is relative to the environment variable VRM_MODELS_DIR, which is helpful beyond the schema's brief description.

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 loads a VRM model file (verb+resource) and distinguishes it from siblings like set_vrm_expression, animate_vrm_bone, etc. The Japanese text is specific.

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?

No guidance on when to use this tool versus siblings, such as when to load a model before setting expressions or poses. No context provided about prerequisites or typical workflow.

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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