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YouCam for Beauty & Personal Care

Ai Look Virtual Try On

AI-Look-Virtual-Try-On

The AI Look Virtual Try-On API provides a complete workflow for applying professionally designed facial looks to user photos. Each look is crafted by beauty experts and can be applied instantly via API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pollingNoIf true (default), keep polling until the task finishes, returning the final result. If false, return immediately without waiting for the task to finish.
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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

Annotations indicate the tool is not read-only and not idempotent, but the description adds little beyond that. It claims 'applied instantly,' which may mislead about the asynchronous polling behavior implied by the 'polling' parameter, and it does not explain the workflow or potential side effects.

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 concise at two sentences and front-loads the core purpose. However, the second sentence about beauty experts is promotional and does not add functional value, slightly reducing efficiency.

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 the complex input schema with two source options and polling, plus many similar sibling tools, the description is too minimal to guide an agent. It does not explain the need for a template or the async behavior, relying entirely on the schema.

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

Parameters2/5

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

The description does not mention any parameters, leaving the schema to carry all meaning. With only 50% schema description coverage, the description should compensate by explaining the request structure (template_id, src_file_url, src_file_id), but it offers nothing.

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 the tool applies professionally designed facial looks to user photos, which is a specific verb-resource combination. However, it does not explicitly differentiate from similar siblings like AI-Makeup-Virtual-Try-On, so it falls short of full clarity.

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 provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites like selecting a template or uploading a file. The only contextual hint is the phrase 'complete workflow,' but there is no explicit usage direction.

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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