AI Development Pipeline MCP

# Deployment Guide This guide covers deploying the AI Development Pipeline MCP Integration to various platforms. ## 🚀 Vercel Deployment (Recommended) ### Prerequisites - Vercel account - GitHub repository - Environment variables configured ### Step-by-Step Deployment 1. **Install Vercel CLI:** ```bash npm install -g vercel ``` 2. **Login to Vercel:** ```bash vercel login ``` 3. **Deploy from local directory:** ```bash vercel deploy ``` 4. **Configure environment variables:** ```bash vercel env add VERCEL_TOKEN vercel env add AIRTABLE_API_KEY vercel env add SQUARE_ACCESS_TOKEN # Add all other environment variables ``` 5. **Deploy to production:** ```bash vercel deploy --prod ``` ### Vercel Configuration Create `vercel.json` in the root directory: ```json { "functions": { "app/api/mcp/route.ts": { "runtime": "@vercel/node" } }, "rewrites": [ { "source": "/api/mcp", "destination": "/app/api/mcp/route.ts" } ] } ``` ## 🐳 Docker Deployment ### Dockerfile Create a `Dockerfile` in the root directory: ```dockerfile FROM node:18-alpine WORKDIR /app COPY package*.json ./ RUN npm ci --only=production COPY . . RUN npm run build EXPOSE 3000 CMD ["npm", "start"] ``` ### Docker Compose Create `docker-compose.yml`: ```yaml version: '3.8' services: mcp-server: build: . ports: - "3000:3000" environment: - NODE_ENV=production env_file: - .env ``` ### Build and Run ```bash # Build the image docker build -t ai-dev-pipeline-mcp . # Run the container docker run -p 3000:3000 --env-file .env ai-dev-pipeline-mcp ``` ## ☁️ AWS Deployment ### AWS Lambda 1. **Install Serverless Framework:** ```bash npm install -g serverless ``` 2. **Create `serverless.yml`:** ```yaml service: ai-dev-pipeline-mcp provider: name: aws runtime: nodejs18.x region: us-east-1 functions: mcp: handler: app/api/mcp/route.handler events: - http: path: mcp method: any ``` 3. **Deploy:** ```bash serverless deploy ``` ## 🌐 Netlify Deployment ### Netlify Functions 1. **Create `netlify.toml`:** ```toml [build] functions = "netlify/functions" [[redirects]] from = "/api/mcp" to = "/.netlify/functions/mcp" status = 200 ``` 2. **Create function in `netlify/functions/mcp.js`:** ```javascript const { handler } = require('../../app/api/mcp/route'); exports.handler = handler; ``` ## 🔧 Environment Configuration ### Production Environment Variables Ensure these are set in your deployment platform: ```env NODE_ENV=production VERCEL_TOKEN=your_production_token AIRTABLE_API_KEY=your_production_key SQUARE_ACCESS_TOKEN=your_production_token ANALYTICS_SECRET=your_production_secret NEXT_PUBLIC_APP_URL=https://your-domain.com ``` ### Security Considerations - Use different API keys for production - Enable HTTPS/SSL certificates - Configure CORS properly - Set up monitoring and logging - Use secrets management services ## 📊 Monitoring and Logging ### Vercel Analytics Enable Vercel Analytics in your dashboard for: - Request metrics - Error tracking - Performance monitoring ### Custom Logging Add logging to your MCP tools: ```typescript console.log(`[${new Date().toISOString()}] Tool executed: ${toolName}`); ``` ### Health Checks Create a health check endpoint: ```typescript // In route.ts if (request.url.includes('/health')) { return new Response('OK', { status: 200 }); } ``` ## 🔄 CI/CD Pipeline ### GitHub Actions Create `.github/workflows/deploy.yml`: ```yaml name: Deploy to Vercel on: push: branches: [main] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-node@v2 with: node-version: '18' - run: npm ci - run: npm run build - uses: amondnet/vercel-action@v20 with: vercel-token: ${{ secrets.VERCEL_TOKEN }} vercel-org-id: ${{ secrets.ORG_ID }} vercel-project-id: ${{ secrets.PROJECT_ID }} ``` ## 🚨 Troubleshooting ### Common Deployment Issues **Build failures:** - Check Node.js version compatibility - Verify all dependencies are listed in package.json - Ensure TypeScript compiles without errors **Runtime errors:** - Check environment variables are set - Verify API endpoints are accessible - Review logs for specific error messages **Performance issues:** - Enable caching where appropriate - Optimize bundle size - Use CDN for static assets ### Getting Help - Check platform-specific documentation - Review deployment logs - Test locally before deploying - Use staging environments for testing ## 📈 Scaling Considerations ### Performance Optimization - Implement caching strategies - Use connection pooling for databases - Optimize API response sizes - Enable compression ### Load Balancing - Use multiple deployment regions - Implement health checks - Configure auto-scaling - Monitor resource usage --- For more deployment options and advanced configurations, refer to the platform-specific documentation.