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YouCam for Fashion & Retail

Ai Ring Virtual Try On

AI-Ring-Virtual-Try-On

Easily Create Your AR Ring or Engagement Ring Try Ons. You Only Need to Upload Images. Opt for 2D images for effortless yet high-quality virtual try-on experiences with minimal effort.

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

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already indicate this is a non-read-only, open-world operation. The description adds that it produces AR-based results and suggests 2D images, but does not disclose asynchronous polling behavior, potential side effects on uploaded data, or time expectations. With annotations present, this is acceptable but not rich.

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 two sentences and front-loads the core purpose. The second sentence is mildly promotional ('effortless yet high-quality') but does not waste much space. It is concise overall.

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 highly complex nested request schema (four union variants, many ring parameters, mask fields), the description is far too thin. It does not explain how to construct a valid request, the polling option, or the nature of the task (e.g., synchronous vs. async). The output schema exists but does not compensate for missing request semantics.

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?

Schema description coverage is only 50%, and the description does not compensate. It only mentions uploading images, providing no insight into the complex 'request' object, optional masks, or the polling parameter. Since half the parameters are undocumented in the schema, the description fails to fill the gap.

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's function: creating AR ring or engagement ring try-ons from uploaded images. This specific verb+resource combination (create + ring try-on) distinguishes it from sibling tools for other jewelry types like necklaces or earrings.

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?

The phrase 'Ring or Engagement Ring Try Ons' implies this tool is for rings, but there is no explicit mention of when to use it vs. alternatives or exclusions. The statement 'You Only Need to Upload Images' offers a basic input requirement but no comparative guidance.

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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TDQS

B3/5.0
Disambiguation5/5

Each AI virtual try-on tool targets a unique product category (bag, bracelet, clothes, etc.) with clear descriptions. The utility tools for upload, cost, and status are distinct in purpose despite some overlap in the upload workflow.

Naming Consistency3/5

The majority of tools follow a consistent 'AI-Product-Virtual-Try-On' pattern, but utility tools break this with mixed styles (e.g., 'File-Upload', 'Get-Upload-API-Info' vs 'upload_file'), creating inconsistency across the entire set.

Tool Count4/5

18 tools is slightly above the ideal range but justified given the diverse product categories. Each try-on tool serves a distinct need, and the utility tools are necessary for the workflow, so the count feels appropriate.

Completeness5/5

The surface covers all major fashion and retail categories for virtual try-on, includes utility tools for file upload, task status, pricing, and templates/patterns, and leaves no obvious gaps for the intended use case.

Resources