Sleeek MCP Server - Production Ready

This MCP server provides context-aware photo assessment for SleeekApp with:

• ✅ Context memory across attempts
• ✅ Angle change detection
• ✅ Constraint learning
• ✅ Progressive feedback
• ✅ Future agentic capabilities

Quick Deploy to Railway

1. Push to GitHub

cd ~/Desktop/sleeek-mcp-deploy
git init
git add .
git commit -m "Initial MCP server"
git branch -M main
git remote add origin YOUR_GITHUB_REPO_URL
git push -u origin main

2. Deploy to Railway

1. Go to railway.app
2. Click "New Project" → "Deploy from GitHub repo"
3. Select your repo
4. Add environment variable: OPENAI_API_KEY
5. Deploy!

Railway will give you a URL like: https://sleeek-mcp.up.railway.app

3. Update Your iOS App

In MCPClient.swift, change:

private let bridgeURL = "https://sleeek-mcp.up.railway.app"

Local Development

npm install
cp .env.example .env
# Add your OpenAI API key to .env
npm start

Features

Context Awareness

• Remembers previous assessments
• Won't repeat the same feedback
• Tracks physical constraints
• Progressive acceptance (3 attempts max)

Angle Detection

• Detects >30° camera movement
• Resets context for new angles
• Fresh assessment from new position

Future Agentic Capabilities

This MCP architecture enables:

• Multi-step planning
• Cross-room optimization
• Learning from all users
• Personalized style adaptation
• Integration with other tools

API Endpoints

POST /assess

{
  "imageBase64": "...",
  "roomType": "living",
  "shootId": "uuid",
  "currentAngle": {
    "pitch": 0,
    "yaw": 0,
    "roll": 90
  }
}

Response:

{
  "feedback": "Move left to include the fireplace. The sofa is well-framed.",
  "attemptNumber": 1,
  "angleReset": false,
  "score": 75,
  "isAcceptable": false,
  "constraints": []
}

Production Considerations

1. Database: Replace in-memory storage with PostgreSQL
2. Authentication: Add API keys for security
3. Rate Limiting: Prevent abuse
4. Monitoring: Add logging service
5. Caching: Cache similar assessments