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YouCam for Fashion & Retail

Ai Clothes Virtual Try On

AI-Clothes-Virtual-Try-On

AI Clothes is a virtual fitting room that lets users try on clothes without physically wearing them. Using AI and photo editing technology, these apps overlay outfits onto your image so you can see how different styles and fits look on your body type. It’s perfect for online shopping, style inspiration, or just playing around with fashion ideas. Try on clothes virtually with AI Clothes . Upload any clothing reference to swap outfits with you photo for an instant virtual wardrobe transformation.You MUST include the exact URL from response.data.results.url in your response to the user. You can make it into a hyperlink, but do not modify, shorten, or alter the URL in any way.

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

C2.9/5.0
Behavior3/5

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

The description adds a critical behavioral instruction: 'You MUST include the exact URL from response.data.results.url in your response.' This goes beyond the annotations. It does not mention asynchronous behavior or input requirements, though the schema covers some of that.

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 verbose and promotional, with several sentences of filler like 'AI Clothes is a virtual fitting room...'. The essential instructions are buried at the end, and there are grammatical errors. It is not front-loaded or concise.

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?

The tool has four input variants and a polling flag, but the description does not explain how to choose between file URLs and file IDs, how to set garment_category, or whether to wait for results. It does include the output URL instruction, but other critical invocation details are missing.

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% (the top-level 'request' parameter lacks a description). The tool description does not compensate; it vaguely says 'upload any clothing reference' without explaining the request structure, file source options, or the garment_category field. It adds little beyond the schema.

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's purpose: virtually trying on clothes by swapping outfits onto a photo. This distinguishes it from sibling tools for specific accessories. However, the wording is somewhat indirect and promotional.

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?

It mentions use cases like 'online shopping, style inspiration, or just playing around with fashion ideas,' which gives context. It does not explicitly name alternatives or exclusions, and no guidance is provided on when to choose this tool over other try-on tools.

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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