mcp-face-transform

face_beautify

Enhance portrait photos by smoothing skin, whitening complexion, slimming the face, and enlarging eyes. Adjust each effect with customizable intensity levels.

Instructions

Apply beauty effects (smoothing / whitening / slimming / eye enlarging).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image_url": "<image_url>"
    },
    "name": "face_beautify"
  }
}

Input Schema

Name	Required	Description
`image_url`	Yes	URL of the portrait to beautify.
`smoothing`	No	Skin smoothing 0-100 (default 10).
`whitening`	No	Whitening 0-100 (default 30).
`face_lifting`	No	Face slimming 0-100 (default 70).
`eye_enlarging`	No	Eye enlarging 0-100 (default 70).

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 18, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden. It only describes what effects are applied but does not disclose behavioral traits such as whether the operation is destructive, how it handles missing faces, whether the original image is modified, or any required permissions. The minimal phrasing leaves significant gaps.

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 a single, efficient sentence that front-loads the core purpose. Every word earns its place, though it lacks structural elements like bullet points or explicit mention of the required image_url parameter. Still, it avoids verbosity.

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 complexity (5 parameters, multiple sibling tools, output schema exists), the description is too brief. It omits context like required input format, output behavior, limitations (e.g., only works on portraits), and comparisons to alternate face tools. The output schema exists, so return values need not be described, but the description still feels incomplete.

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?

Schema coverage is 100% and each parameter already has a descriptive schema (name, range, default). The description adds no new semantic meaning beyond enumerating the effects, which is already evident from parameter names and descriptions. Baseline 3 is appropriate.

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 purpose with a specific verb ('Apply') and resource ('beauty effects'), listing four concrete effects in parentheses. This distinguishes it from sibling tools like face_cartoonize or face_swap, as beautification is a distinct operation.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites (e.g., image must contain a face), nor any exclusion criteria or comparison with siblings like face_cartoonize. The agent is left to infer use cases.

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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