sleeek-mcp-server

ShhhShaq/sleeek-mcp-server

3.1

If you are the rightful owner of sleeek-mcp-server and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

The Sleeek MCP Server is a production-ready context-aware photo assessment server designed for SleeekApp, offering advanced features like context memory, angle detection, and constraint learning.

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