io.github.AceDataCloud/mcp-face-transform

Official

by AceDataCloud · Python

AceDataCloud Face Transform MCP: keypoints, beautify, age/gender, swap, cartoon, liveness

Face transformation tools: keypoints, beautification, age/gender detection, swap, cartoon, liveness.

README.md3 KBView on GitHub

MCP Face Transform Server

A Model Context Protocol (MCP) server that exposes the AceDataCloud Face Transform API — face keypoint detection, beautification, age/gender transform, face swap, cartoonization, and liveness detection.

Status: All Face APIs are currently in Alpha. Interfaces may evolve.

Features

Keypoint detection — 90+ landmarks per face, multi-face supported
Beautification — smoothing, whitening, face slimming, eye enlarging
Age transform — age or de-age a portrait
Gender transform — swap perceived facial gender characteristics
Face swap — move a source face onto a target image (with optional async webhook)
Cartoonize — render a portrait in animated / cartoon style
Liveness detection — distinguish live captures from printed / screen photos

Installation

bash

pip install mcp-face-transform

Configuration

Set your AceDataCloud API token:

bash

export ACEDATACLOUD_API_TOKEN=your_token_here

Get your token from https://platform.acedata.cloud.

Usage

stdio mode (default)

bash

mcp-face-transform

HTTP mode

bash

mcp-face-transform --transport http --port 8000

Tool Reference

Tool	Description
`face_detect_keypoints`	Detect 90+ keypoints per face (multi-face supported).
`face_beautify`	Smoothing, whitening, face slimming, and eye enlarging.
`face_change_age`	Age or de-age a portrait.
`face_change_gender`	Swap perceived facial gender characteristics.
`face_swap`	Move a source face onto a target image (with optional async webhook).
`face_cartoonize`	Render a portrait in cartoon / animated style.
`face_detect_liveness`	Distinguish a live capture from a printed / screen photo.
`face_get_usage_guide`	Concise client-side tool usage reference.

Example

text

"Detect all faces in https://example.com/group.jpg and return their keypoints."
→ face_detect_keypoints(image_url="https://example.com/group.jpg")

"Lighten and smooth my portrait."
→ face_beautify(image_url="https://example.com/me.jpg", smoothing=15, whitening=25)

"Replace the face in the scene with the headshot."
→ face_swap(
    source_image_url="https://example.com/headshot.jpg",
    target_image_url="https://example.com/scene.jpg",
  )

Configuration in Claude Desktop / Claude Code

json

{
  "mcpServers": {
    "face-transform": {
      "command": "uvx",
      "args": ["mcp-face-transform"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Or use the hosted endpoint with bearer auth:

json

{
  "mcpServers": {
    "face-transform": {
      "url": "https://face.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer your_api_token_here"
      }
    }
  }
}

Development

bash

pip install -e ".[dev,test]"
pytest --cov=core --cov=tools
ruff check .

License

MIT — see LICENSE.

Install

Configuration

Environment variables

ACEDATACLOUD_API_TOKENrequiredsecret

API token from Ace Data Cloud (https://platform.acedata.cloud)

claude_desktop_config.json

{
  "mcpServers": {
    "mcp-face-transform": {
      "command": "uvx",
      "args": [
        "mcp-face-transform"
      ],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "<YOUR_ACEDATACLOUD_API_TOKEN>"
      }
    }
  }
}