API & Integration Layer

Full REST API and Model Context Protocol (MCP) integration layer for extending CutCutBoom beyond the dashboard.

REST API Endpoints

Jobs

GET /api/jobs              # List all jobs
POST /api/jobs             # Create new job
GET /api/jobs/:id          # Get job details
PATCH /api/jobs/:id        # Update job
DELETE /api/jobs/:id       # Cancel job
GET /api/jobs/:id/history  # Get job execution log

Devices

GET /api/devices           # List connected devices
GET /api/devices/:id       # Get device status
POST /api/devices/:id/jog  # Send jog command
POST /api/devices/:id/home # Go to origin

Queue

GET /api/queue             # Get queue status
POST /api/queue/resume     # Resume processing
POST /api/queue/pause      # Pause queue
POST /api/queue/clear      # Clear pending jobs

MCP Integration

CutCutBoom exposes an MCP server endpoint that allows Claude, Copilot, and other AI assistants to:

• Query job history and current status
• Submit new cutting jobs with natural language
• Monitor hardware status
• Get recommendations based on past jobs

Scripting Examples

Node.js

const cutcutboom = require('cutcutboom-client');
const client = new cutcutboom.Client('http://localhost:3200');
await client.jobs.create({
  path: '/jobs/logo.hpgl',
  speed: 75,
  device: 'vinyl-1'
});

Python

import requests
response = requests.post('http://localhost:3200/api/jobs', json={
    'path': '/jobs/logo.hpgl',
    'speed': 75,
    'device': 'vinyl-1'
})

Workflow Examples

• Batch Processing: Process 100 jobs overnight with custom speed ramps
• Conditional Workflow: “If job fails, retry on device 2”
• Analytics Integration: Send job metrics to your dashboards
• Multi-machine Orchestration: Distribute jobs across devices based on load