3DiVi Face SDK — MCP Server

The face recognition MCP server for AI-native development.
On-premise · GDPR-compliant · ISO 30107-3 liveness · No cloud dependency.

What this is

A local Model Context Protocol server that wraps the 3DiVi Face SDK Processing Block API as callable tools. AI coding agents (Claude Code, Cursor, Windsurf, VS Code Copilot) can detect faces, verify identity, run liveness checks, and search a face gallery — without writing a single line of SDK integration code.

All processing runs on the developer's machine. No data leaves the host.

Tools exposed

Tool	Processing Block	Output
`detect_faces`	`FACE_DETECTOR`	Bounding boxes, landmarks, pitch/yaw/roll
`check_liveness`	`LIVENESS_ESTIMATOR`	Liveness verdict + confidence
`assess_quality`	`QUALITY_ASSESSMENT_ESTIMATOR`	Quality score, eyes-open flag
`extract_template`	`FACE_TEMPLATE_EXTRACTOR`	ContextTemplate (binary, no PII)
`verify_face`	`VERIFICATION_MODULE`	1:1 similarity score + match verdict
`search_face`	`MATCHER_MODULE` + `DynamicTemplateIndex`	1:N ranked match list
`estimate_attributes`	`AGE_ESTIMATOR` + `GENDER_ESTIMATOR`	Age, gender, confidence
`estimate_emotion`	`EMOTION_ESTIMATOR`	7-class emotion probabilities
`detect_mask`	`MASK_ESTIMATOR`	Masked/unmasked verdict

Quick start

Prerequisites

3DiVi Face SDK 3.29 installed on your machine
Python 3.9+
A valid Face SDK licence

Install

git clone https://github.com/3divi/face-recognition-mcp-server.git
cd face-recognition-mcp-server
pip install -r server/python/requirements.txt

Configure Claude Code

Add to your claude_desktop_config.json (or equivalent for your agent):

{
  "mcpServers": {
    "3divi-face-sdk": {
      "command": "python",
      "args": ["/path/to/face-recognition-mcp-server/server/python/main.py"],
      "env": {
        "FACESDK_PATH": "/path/to/facesdk",
        "FACESDK_CONFIG": "/path/to/facesdk/conf/facerec.conf"
      }
    }
  }
}

Try it

Open Claude Code in any project and type:

Integrate 3DiVi Face SDK for KYC verification — detect the face, run liveness,
extract a template, and compare against an enrolled template.

Claude Code will call the MCP tools directly. No SDK integration code required.

Project structure

face-recognition-mcp-server/
├── CLAUDE.md                        # Machine-readable API reference (read by AI agents)
├── README.md
├── LICENSE
├── server/
│   ├── python/                      # Phase 1 — stdio MCP server
│   │   ├── main.py                  # Entry point (JSON-RPC 2.0 over stdio)
│   │   ├── tools/
│   │   │   ├── detection.py         # detect_faces, check_liveness, assess_quality, detect_mask
│   │   │   ├── recognition.py       # extract_template, verify_face, search_face
│   │   │   └── attributes.py        # estimate_attributes, estimate_emotion
│   │   └── requirements.txt
│   └── nodejs/                      # Phase 2 — coming soon
├── reference-apps/
│   ├── kyc-onboarding/              # Phase 2
│   ├── access-control/              # Phase 2
│   └── emotion-analytics/           # Phase 3
└── docs/
    └── threshold-guide.md

Why on-premise matters

Privacy regulations (GDPR, BIPA, India DPDP) prohibit sending biometric data to third-party cloud APIs in many regulated industries. This MCP server runs entirely local — the SDK shared libraries process images in-process, and no biometric data is transmitted anywhere.

The only competing biometric MCP server in the registry is Microsoft Azure's liveness wrapper, which requires Azure cloud. This server requires nothing beyond the SDK licence you already have.

Similarity threshold guide

See docs/threshold-guide.md for use-case-specific recommendations.

Use case	Recommended threshold
Physical access control	0.97
Enterprise time/attendance	0.95
KYC / onboarding	0.93
Visitor watchlist	0.90
Retail re-identification	0.85

Licence

MIT — the MCP server is open source. The underlying 3DiVi Face SDK requires a separate licence from 3divi.ai.

face-recognition-mcp-server