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lvncer

vrmcp

by lvncer

set_vrm_expression

Animates a VRM model's facial expression by selecting an emotion (e.g., happy, angry) and setting its intensity from 0.0 to 1.0.

Instructions

VRMモデルの表情を設定する

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
weightYes表情の強さ (0.0-1.0)
expressionYes設定する表情(例: happy, angry, sad, surprised, neutral)
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral details such as whether the expression overrides previous ones, is additive, or requires a loaded model. Minimal disclosure for a mutation operation.

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 a single concise sentence without wasted words, front-loading the 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?

Given the simple tool with 2 parameters and no output schema, the description is adequate but lacks behavioral details that would make it completely clear to an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions. The tool description adds no extra meaning beyond what the schema provides. Baseline 3 is appropriate.

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 'Set expression of VRM model' in Japanese, specifying the verb (set) and resource (expression). It differentiates from sibling tools like set_vrm_pose and animate_vrm_bone.

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?

No explicit when-to-use or when-not-to-use guidance is provided. Context is implied through sibling tool names, but no exclusions or alternatives are mentioned.

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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