AI-Makeup-Virtual-Try-On-Pattern-Name
Get AI Makeup VTO available pattern names.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| categories | Yes | List of makeup VTO categories. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Get AI Makeup VTO available pattern names.
| Name | Required | Description | Default |
|---|---|---|---|
| categories | Yes | List of makeup VTO categories. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no additional behavioral context, such as how categories affect results or any rate limits, but it does not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no filler or redundant content. Every word contributes to the purpose, and it is front-loaded with the action verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter, complete schema, and annotations. The output schema exists, so return values need not be explained. However, the description lacks any broader workflow context, such as when fetching pattern names is necessary, which is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; the categories parameter is fully described with an enum of allowed values. The description adds no extra meaning about parameter semantics, hitting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action and resource: 'Get AI Makeup VTO available pattern names.' It explicitly identifies the tool as a getter for pattern names specific to makeup VTO, distinguishing it from sibling VTO tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, typical use cases, or exclusions, leaving the agent to infer usage from the schema alone.
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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