Face-Recognition-MCP-Server: Instructions file for Claude Code

Face SDK — AI Agent Reference

3DiVi Face SDK v3.29 · Processing Block API · Local MCP server
This file is read automatically by Claude Code, Cursor, and Windsurf.
It contains everything an AI agent needs to generate correct SDK integration code.

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Face SDK Overview

3DiVi Face SDK v3.29 provides on-premise biometric processing. All computation runs locally — no data leaves the machine. Two API families exist:

• Processing Block API (v3.29) — context-based, use this for all new integrations
• Legacy API — deprecated, do not use

Primary language: Python. C++, Java, C#, Node.js also supported.

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Initialisation

import face_sdk_3divi as F3

# REQUIRED: first arg = SDK root directory (contains lib/, conf/)
# REQUIRED: config_dir = path to facerec.conf file
service = F3.create_service(
    './facesdk',                       # SDK root
    config_dir='./conf/facerec.conf'   # config file path — not the directory
)

SDK directory layout (must match the path you pass to create_service()):

./facesdk/
├── lib/            # shared libraries (.so / .dll / .dylib)
├── conf/
│   └── facerec.conf    # pass this full path to config_dir
└── license/        # licence file (auto-detected from conf/)

Common mistake: passing './conf' instead of './conf/facerec.conf' raises a licence error.

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Context container

Every Processing Block reads from and writes to a Context object — a typed key-value store (analogous to JSON). It is the only way to pass data between blocks.

# Create
ctx = service.create_context()

# Populate input — nested key access with [][]
ctx['image']['blob']   = open('photo.jpg', 'rb').read()  # raw bytes
ctx['image']['format'] = 'JPEG'   # 'JPEG' | 'PNG' | 'BMP' | 'TIFF'

# Call a processing block (modifies ctx in-place)
detector = service.create_processing_block({'unit_type': 'FACE_DETECTOR'})
detector(ctx)

# Read output
faces = ctx['output_data']   # list of face objects
if faces:
    bbox = faces[0]['bbox']  # dict: x, y, width, height