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YouCam for Fashion & Retail

Ai Scarf Virtual Try On

AI-Scarf-Virtual-Try-On

Enhance your fashion experience with the online AR Scarf Virtual Try-On. Shoppers can instantly drape scarves over their outfits and see how patterns flow in real life. This interactive virtual scarf feature allows customers to explore different styles and colors online, replicating the in-store experience. Powered by high-fidelity AR simulation, users can enjoy detailed scarf visualisation anytime, anywhere.gender must be explicitly provided by the user. If missing, ask the user for clarification and set the parameter once received.

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

B3.3/5.0
Behavior3/5

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

The description adds context about AR simulation and high-fidelity visualization, but it does not disclose operational traits such as asynchronous execution, polling behavior, or the nature of the output (e.g., a generated image). Annotations already mark the tool as not read-only and not destructive, and the description does not contradict them. It also includes a behavioral requirement for gender, which is useful context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is padded with marketing language like 'Enhance your fashion experience' and 'high-fidelity AR simulation,' repeating the concept of virtual try-on multiple times. The useful instruction about gender is appended awkwardly after a space, forming 'anywhere.gender' typo. It could be trimmed to one or two sentences focusing on purpose and the gender requirement.

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 four anyOf variants, a polling parameter, and an output schema, the description is far too high-level. It omits guidance on choosing between URL and ID inputs, the asynchronous nature of the task, and the expected result. The gender requirement is the only operational detail provided, which is insufficient for an agent to confidently invoke this tool.

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?

The description explicitly stresses that gender must be provided and instructs the agent to ask if missing, adding semantic weight to that parameter beyond the schema's required flag. However, it does not clarify the meaning of src_file_url vs ref_file_url or the file ID variants, and with only 50% schema description coverage, it does not fully compensate. The mention of 'styles and colors' loosely aligns with the style parameter but adds little detail.

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 identifies the tool as a virtual scarf try-on service, using active language like 'drape scarves over their outfits' and 'see how patterns flow in real life.' This distinguishes it from sibling tools for other accessories/clothing. The resource (scarf) and action (virtual try-on) are both explicit, earning a 5.

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 description implies usage for shoppers interested in virtually trying on scarves, but it does not explicitly contrast with sibling try-on tools or state when not to use it. It does provide a clear operational guideline that 'gender must be explicitly provided' and instructs the agent to ask for clarification if missing, which is helpful. However, this is not about tool selection, so the overall guidance is only implied rather than explicit.

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