YouCam for Beauty & Personal Care

Ai Hair Color Virtual Try On

AI-Hair-Color-Virtual-Try-On

Explore a wide range of hair colors with our hair color changer! Try the hair color you've always dreamed of and experiment with new shades you’ve never tried before. Easily adjust the intensity of your chosen color with sliders for a customized look.

Upload Your Image Upload the photo you want to change hair color for.
Choose Preset Colors or Customize by Pattern and Palettes Choose from predefined color presets or fine tune by adjusting the ombre coverage and blend for unlimited possibilities!

Warning: If both a preset and pattern + palettes are specified, the preset will take priority. Warning: Your source image needs to contain the hair section for dyeing, so double-check before applying. Make sure your source image includes the hair area you want to dye — it's your responsibility to get it right.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`polling`	No	If true (default), keep polling until the task finishes, returning the final result. If false, return immediately without waiting for the task to finish.
`request`	Yes	This object represents a run AI Hair Color Virtual Try-On task.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4/5.0

Behavior4/5

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

The description adds valuable behavioral warnings beyond the annotations: the preset-priority conflict and the requirement that the source image contain the hair area. These caveats help the agent anticipate failure modes. However, it does not mention async processing or polling, though the schema covers that.

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

Conciseness3/5

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

The description is structured with bullet points and warnings, but the opening sentences are promotional ('Explore a wide range...', 'Try the hair color you've always dreamed of') and do not provide functional information. The length could be reduced without losing value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential workflow and key constraints (image must contain hair, preset priority). The input schema provides detailed parameter documentation, and the output schema handles return values, so the description is sufficient for an agent to use the tool effectively without missing critical information.

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

Parameters4/5

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

With 100% schema description coverage, the baseline is 3. The description adds extra meaning by explaining the priority relationship between preset and pattern/palettes, and by referencing sliders for adjusting color intensity, which maps to color_intensity and shine_intensity. This goes beyond the schema's own descriptions.

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 hair color changer: 'hair color changer', 'Try the hair color you've always dreamed of', and 'Choose from predefined color presets or fine tune...'. It is specific about the resource (hair color) and distinguishes it from sibling tools like AI-Hair-Style-Virtual-Try-On.

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 explains the workflow (upload image, choose preset or pattern/palettes) and gives warnings, but it does not explicitly state when to use this tool versus alternatives. Usage is implied through context, but no exclusions or alternative comparisons are provided.

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

C2.7/5.0

Disambiguation3/5

Most tools target distinct features (abs, aging, bangs, beard, etc.), but there are several overlapping pairs like AI-Face-Lift vs AI-Face-Reshape and AI-Skin-Analysis vs AI-Skin-simulation that could confuse an agent. The '-Detection' and '-Templates' suffixes help, but the sheer number of similar virtual try-on tools makes selection less obvious.

Naming Consistency2/5

The majority of tools follow an 'AI-<Feature>-<Action>' pattern with hyphens, but there are clear inconsistencies: 'Get-Feature-Cost', 'Get-Running-Task-Status', and 'File-Upload' break the pattern, and 'upload_file' uses snake_case. The mixture of 'Filter', 'Simulator', 'Generator', 'Virtual-Try-On', and 'Detection' suffixes also lacks a standardized verb/noun structure.

Tool Count2/5

With 46 tools, the server exceeds the 25-tool threshold and feels bloated. Many 'Templates' tools (e.g., AI-Bangs-Filter-Virtual-Try-On-Templates) and separate detection/action pairs add redundancy. The scope is broad but could be consolidated into fewer, more generic tools.

Completeness4/5

The server covers a wide range of beauty and personal care features: hair, face, skin, body, makeup, and nails, including both try-on and detection/analysis capabilities. Minor gaps exist (e.g., no eyelash try-on), but the surface is quite comprehensive for the stated purpose, with no major dead ends.

Resources

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