Notepad++ MCP Server

# 📊 AI-Powered Monitoring Stack Deployment **From Specialist Territory to 5-Minute Setup** **Grafana + Prometheus + Loki + Promtail with AI + Docker** **Timeline**: September 2025 --- ## 🎯 The Monitoring Revolution ### **Traditional Reality (Before AI + Docker)** - ❌ **Specialist required**: Dedicated DevOps/SRE person on team - ❌ **Weeks of setup**: Complex configuration, networking, storage - ❌ **Documentation hell**: Scattered configs, version conflicts - ❌ **Maintenance burden**: Updates, backups, scaling issues - ❌ **Enterprise-only**: Too complex for home/small projects ### **AI + Docker Reality (Now)** - ✅ **5-minute deployment**: AI generates complete docker-compose stack - ✅ **Anyone can do it**: No specialist knowledge required - ✅ **Production-ready**: Proper configs, networking, persistence - ✅ **Impressive results**: Professional dashboards immediately - ✅ **Home surveillance ready**: Perfect for unconventional monitoring --- ## 🏠 Perfect Use Cases ### **1. Home Surveillance & Control Systems** **What you can monitor**: - 🏠 **Smart home devices**: Nest Protect, thermostats, cameras - 📡 **Network infrastructure**: Router logs, bandwidth, connectivity - 🔋 **IoT sensors**: Temperature, humidity, motion, door/window states - 🚗 **Vehicle tracking**: GPS, fuel, maintenance alerts - 💡 **Energy consumption**: Solar panels, battery banks, usage patterns **Why it's game-changing**: - ✅ **Centralized view**: All your home systems in one dashboard - ✅ **Historical analysis**: Trends, patterns, anomaly detection - ✅ **Real-time alerts**: Slack/email notifications for issues - ✅ **Mobile access**: Check your home from anywhere ### **2. Development Project Monitoring** **For projects like our nest-protect MCP**: - 📈 **API call patterns**: Nest API usage, rate limiting, errors - 🔧 **Tool performance**: Response times, success rates - 🚨 **Error tracking**: Import failures, authentication issues - 📊 **Usage analytics**: Which tools are used most, user patterns **For full-stack projects like veogen**: - 🌐 **Frontend metrics**: Page load times, user interactions - ⚡ **Backend performance**: API response times, database queries - 💾 **Infrastructure health**: Memory usage, CPU, disk space - 🔄 **Deployment tracking**: Build times, deployment success rates ### **3. The "Impress the Neighbors" Factor** **Professional-looking dashboards for**: - 🏡 **Home energy efficiency**: Solar production vs. consumption graphs - 🌡️ **Climate monitoring**: Multi-room temperature/humidity trends - 🚗 **Vehicle fleet tracking**: Family car locations and stats - 📺 **Media server analytics**: Streaming usage, storage trends - 🌐 **Network performance**: Internet speed tests, uptime monitoring --- ## 🚀 The 5-Minute AI Setup ### **Prompt Template for AI** ``` Create a complete monitoring stack with Grafana, Prometheus, Loki, and Promtail using Docker Compose. REQUIREMENTS: - Grafana dashboards for home surveillance and IoT monitoring - Prometheus for metrics collection - Loki for log aggregation - Promtail for log shipping - Persistent storage for all data - Proper networking between services - Pre-configured dashboards for common home monitoring scenarios - Include example configurations for: * Smart home devices (Nest, sensors) * Network infrastructure monitoring * Application performance monitoring * System resource monitoring Make it production-ready but easy to customize for home use. ``` ### **What AI Generates (Example)** ```yaml # docker-compose.yml - Complete monitoring stack version: '3.8' services: grafana: image: grafana/grafana:latest container_name: grafana ports: - "3000:3000" environment: - GF_SECURITY_ADMIN_PASSWORD=admin123 - GF_USERS_ALLOW_SIGN_UP=false volumes: - grafana-data:/var/lib/grafana - ./grafana/dashboards:/etc/grafana/provisioning/dashboards - ./grafana/datasources:/etc/grafana/provisioning/datasources networks: - monitoring prometheus: image: prom/prometheus:latest container_name: prometheus ports: - "9090:9090" command: - '--config.file=/etc/prometheus/prometheus.yml' - '--storage.tsdb.path=/prometheus' - '--web.console.libraries=/etc/prometheus/console_libraries' - '--web.console.templates=/etc/prometheus/consoles' - '--storage.tsdb.retention.time=200h' - '--web.enable-lifecycle' volumes: - ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml - prometheus-data:/prometheus networks: - monitoring loki: image: grafana/loki:latest container_name: loki ports: - "3100:3100" command: -config.file=/etc/loki/local-config.yaml volumes: - ./loki/loki-config.yml:/etc/loki/local-config.yaml - loki-data:/loki networks: - monitoring promtail: image: grafana/promtail:latest container_name: promtail volumes: - /var/log:/var/log:ro - ./promtail/promtail-config.yml:/etc/promtail/config.yml command: -config.file=/etc/promtail/config.yml networks: - monitoring # Node Exporter for system metrics node-exporter: image: prom/node-exporter:latest container_name: node-exporter ports: - "9100:9100" volumes: - /proc:/host/proc:ro - /sys:/host/sys:ro - /:/rootfs:ro command: - '--path.procfs=/host/proc' - '--path.rootfs=/rootfs' - '--path.sysfs=/host/sys' - '--collector.filesystem.mount-points-exclude=^/(sys|proc|dev|host|etc)($$|/)' networks: - monitoring volumes: grafana-data: prometheus-data: loki-data: networks: monitoring: driver: bridge ``` ### **Pre-configured Dashboards AI Creates** **1. Home IoT Dashboard**: ```json { "dashboard": { "title": "Smart Home Overview", "panels": [ { "title": "Nest Protect Status", "type": "stat", "targets": [ { "expr": "nest_protect_battery_level" } ] }, { "title": "Temperature Trends", "type": "graph", "targets": [ { "expr": "temperature_sensor{room=~\".*\"}" } ] }, { "title": "Security System Status", "type": "singlestat", "targets": [ { "expr": "security_system_armed" } ] } ] } } ``` **2. Network Monitoring Dashboard**: - Internet speed tests over time - Router CPU/memory usage - Connected device counts - Bandwidth usage by device - DNS response times **3. Application Performance Dashboard**: - API response times - Error rates - Database query performance - Memory/CPU usage - Active user counts --- ## 🔧 Integration Examples ### **Monitoring Our nest-protect MCP Server** **Metrics to track**: ```python # Add to fastmcp_server.py from prometheus_client import Counter, Histogram, Gauge, start_http_server # Metrics tool_calls_total = Counter('mcp_tool_calls_total', 'Total tool calls', ['tool_name']) tool_duration = Histogram('mcp_tool_duration_seconds', 'Tool execution time', ['tool_name']) active_connections = Gauge('mcp_active_connections', 'Active MCP connections') nest_api_calls = Counter('nest_api_calls_total', 'Nest API calls', ['endpoint', 'status']) @app.tool() async def list_devices() -> Dict[str, Any]: tool_calls_total.labels(tool_name='list_devices').inc() with tool_duration.labels(tool_name='list_devices').time(): try: result = await device_list_func() nest_api_calls.labels(endpoint='devices', status='success').inc() return result except Exception as e: nest_api_calls.labels(endpoint='devices', status='error').inc() raise # Start metrics server start_http_server(8000) ``` **Promtail config for MCP logs**: ```yaml server: http_listen_port: 9080 positions: filename: /tmp/positions.yaml clients: - url: http://loki:3100/loki/api/v1/push scrape_configs: - job_name: mcp-server static_configs: - targets: - localhost labels: job: mcp-server __path__: /var/log/nest-protect-mcp/*.log ``` ### **Home Surveillance Integration** **Monitoring multiple systems**: ```python # Home monitoring agent import psutil import requests from prometheus_client import Gauge # System metrics cpu_usage = Gauge('home_cpu_usage_percent', 'CPU usage') memory_usage = Gauge('home_memory_usage_percent', 'Memory usage') disk_usage = Gauge('home_disk_usage_percent', 'Disk usage') internet_speed = Gauge('home_internet_speed_mbps', 'Internet speed') # IoT device metrics nest_protect_battery = Gauge('nest_protect_battery_percent', 'Battery level', ['device_id']) thermostat_temp = Gauge('thermostat_temperature_celsius', 'Temperature', ['location']) security_status = Gauge('security_system_armed', 'Security system status') async def collect_metrics(): # System metrics cpu_usage.set(psutil.cpu_percent()) memory_usage.set(psutil.virtual_memory().percent) disk_usage.set(psutil.disk_usage('/').percent) # Internet speed test speed = await test_internet_speed() internet_speed.set(speed) # Nest device data devices = await get_nest_devices() for device in devices: nest_protect_battery.labels(device_id=device['id']).set(device['battery']) # Security system security_status.set(1 if await is_security_armed() else 0) ``` --- ## 🎨 Dashboard Examples ### **1. Home Overview Dashboard** **Panels include**: - 🏠 **House temperature**: Multi-room trends - 🔋 **Device battery levels**: All IoT devices - 🌐 **Internet performance**: Speed tests, uptime - 🚨 **Security status**: Armed/disarmed, sensor states - ⚡ **Energy usage**: Solar production vs. consumption - 📱 **Device connectivity**: Online/offline status ### **2. Technical Performance Dashboard** **For the tech-savvy neighbor**: - 📊 **API performance**: Response times, error rates - 💾 **System resources**: CPU, memory, disk usage - 🔄 **Background jobs**: Success rates, queue depths - 📈 **Growth metrics**: Data volume, user activity - 🚀 **Deployment stats**: Build times, success rates ### **3. Fun & Impressive Dashboard** **Show-off features**: - 🌡️ **Weather correlation**: Indoor vs. outdoor temps - 🚗 **Vehicle tracking**: Family car locations - 📺 **Media server stats**: What's being watched - 🏃 **Fitness tracking**: Step counts, activity levels - 🌞 **Solar efficiency**: Production forecasts vs. actual --- ## 🚀 Advanced Use Cases ### **Unconventional Home Monitoring** **Garden automation**: - Soil moisture sensors - Automatic watering system logs - Weather correlation analysis - Plant growth tracking **Pet monitoring**: - Pet door activity logs - Food/water level sensors - Temperature in pet areas - Activity pattern analysis **Energy optimization**: - Smart plug power monitoring - HVAC efficiency tracking - Solar panel performance - Battery bank status ### **Neighborhood Network** **Community monitoring**: - Shared internet performance data - Local weather station network - Community garden sensors - Neighborhood watch integration --- ## 💡 AI Prompts for Specific Setups ### **For Home Surveillance** ``` Create Grafana dashboards for home surveillance monitoring with: - Multi-camera status and storage usage - Motion detection event timeline - Door/window sensor activity - Internet connectivity for remote access - Storage capacity and retention policies - Mobile-friendly responsive design Include alerting rules for: - Camera offline events - Storage capacity warnings - Unusual activity patterns - Internet connectivity issues ``` ### **For Smart Home Integration** ``` Build monitoring for smart home ecosystem: - Nest thermostat temperature control efficiency - Smart lighting usage patterns and energy consumption - Voice assistant query logs and response times - Smart plug power monitoring and automation - HVAC system performance and energy usage - Security system arm/disarm patterns Create predictive dashboards for: - Energy usage forecasting - HVAC optimization recommendations - Security pattern analysis - Device maintenance scheduling ``` ### **For Development Projects** ``` Create developer-focused monitoring for MCP servers: - Tool execution performance and error rates - API call patterns and rate limiting - Authentication success/failure tracking - Resource usage and scaling recommendations - User activity patterns and popular tools - Integration health with external services Include debugging dashboards for: - Real-time error investigation - Performance bottleneck identification - API response time analysis - Memory and CPU profiling ``` --- ## 🎯 Getting Started Checklist ### **Phase 1: Basic Setup (5 minutes)** - [ ] Ask AI to generate docker-compose monitoring stack - [ ] Customize service ports and passwords - [ ] Start with `docker-compose up -d` - [ ] Access Grafana at http://localhost:3000 ### **Phase 2: Data Sources (10 minutes)** - [ ] Configure Prometheus data source in Grafana - [ ] Configure Loki data source for logs - [ ] Import pre-built dashboards - [ ] Verify metrics are flowing ### **Phase 3: Custom Monitoring (30 minutes)** - [ ] Add application metrics to your projects - [ ] Configure log shipping with Promtail - [ ] Create custom dashboards for your use case - [ ] Set up alerting rules ### **Phase 4: Advanced Features (ongoing)** - [ ] Set up mobile notifications - [ ] Create predictive analytics - [ ] Add external integrations (Slack, email) - [ ] Build custom exporters for IoT devices --- ## 🏆 Success Stories ### **Home Automation Dashboard** "Went from scattered IoT device apps to unified monitoring in one afternoon. Now I can see everything from solar production to pet door activity in one place. The neighbors are definitely impressed!" ### **Development Project Monitoring** "Added comprehensive monitoring to our MCP server project. Now we can see API performance, error patterns, and usage analytics. What used to require a dedicated DevOps person took me 30 minutes with AI assistance." ### **Small Business Infrastructure** "Set up professional-grade monitoring for our office without hiring specialists. Track everything from internet performance to coffee machine usage. Looks like enterprise-grade infrastructure!" --- ## 💡 Pro Tips ### **Security Considerations** - Change default passwords immediately - Use environment variables for sensitive configs - Set up proper firewall rules if exposing externally - Enable SSL/TLS for production deployments ### **Performance Optimization** - Set appropriate retention policies for logs and metrics - Use recording rules for frequently queried metrics - Implement downsampling for long-term storage - Monitor the monitoring stack resource usage ### **Maintenance** - Regular backup of Grafana dashboards and configs - Update container images periodically - Monitor disk usage for time-series data - Set up monitoring for the monitoring stack itself --- ## 🎯 The Bottom Line **Traditional approach**: Hire specialist → Weeks of setup → Ongoing maintenance burden **AI + Docker approach**: 5-minute prompt → Professional monitoring → Impressive results **Perfect for**: - ✅ Home surveillance and automation - ✅ Development project monitoring - ✅ Small business infrastructure - ✅ Learning DevOps concepts - ✅ Impressing technically-minded friends! **The game-changer**: What used to be specialist territory is now accessible to anyone willing to learn a few Docker commands and write good AI prompts. The results look professional, work reliably, and provide genuine value for both technical projects and home automation systems. **Remember**: The hard part isn't the technology anymore - it's deciding what interesting things to monitor! 📊🏠🚀