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Sleeek MCP Server - Production Ready

Sleeek MCP Server - Production Ready

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github.com/ShhhShaqMedia & Design0Updated 73w ago

About Sleeek MCP Server - Production Ready

SleeekApp's MCP server assesses photos with context carried across attempts. It tracks camera-angle changes and constraints to provide progressive feedback for photo assessment.

Key features

  1. 01Retains context across assessments
  2. 02Avoids repeating prior feedback
  3. 03Tracks physical constraints
  4. 04Detects camera-angle changes
  5. 05Progressive feedback across up to three attempts

Use cases

  1. 01Assess successive photo attempts with prior context
  2. 02Request a fresh assessment after changing camera angle

MCP support

The MCP server exposes photo assessment for SleeekApp through the `/assess` endpoint, returning feedback, attempt number, angle-reset status, score, acceptability, and constraints.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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

Comments

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