mcp-face-transform
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ACEDATACLOUD_API_TOKEN | Yes | Your AceDataCloud API token, get from https://platform.acedata.cloud |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| face_detect_keypointsA | Detect faces in an image and return 90+ keypoints per face. |
| face_beautifyB | Apply beauty effects (smoothing / whitening / slimming / eye enlarging). |
| face_change_ageA | Age or de-age one or more faces using explicit target age information. |
| face_change_genderB | Swap perceived facial gender characteristics in a portrait. |
| face_swapB | Replace the face in |
| face_cartoonizeB | Convert a portrait to an animated / cartoon style. |
| face_detect_livenessA | Decide whether a face image is from a live person versus a printed / screen photo. |
| face_get_usage_guideA | Return a concise usage guide for the Face Transform tools. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| face_guide | Guide for choosing the right Face Transform tool. |
| face_workflow_examples | Common workflow examples for face analysis and transformation. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Each tool targets a distinct face transformation or detection task. There is no overlap: age change, cartoonize, keypoint detection, beautification, gender change, swap, liveness detection, and a usage guide all have clearly different purposes.
All tool names follow a consistent 'face_' prefix followed by a descriptive verb or verb phrase in snake_case (e.g., face_change_age, face_detect_keypoints). This pattern makes the tool set easy to navigate and select from.
With 8 tools, the server is well-scoped for face-related transformations and detections. Each tool addresses a specific and non-trivial capability, and the count feels appropriate for a focused image processing domain.
The tool set covers the most common face manipulation requests (aging, cartoonizing, beautifying, gender swap, swapping) and detection needs (keypoints, liveness). The included usage guide is a nice touch. A minor gap could be the absence of a general face_generate tool, but the core workflow is well-covered.