comblox/lab-automation-mcp

🤖 Lab Automation MCP Server

Comprehensive Model Context Protocol (MCP) server for intelligent lab environment automation, project management, and infrastructure orchestration.

🎯 Overview

The Lab Automation MCP Server is an intelligent system that understands your entire lab infrastructure and automates complex tasks through natural language interactions. It seamlessly integrates with:

• Proxmox VE Cluster: 3-node virtualization infrastructure
• Network Infrastructure: UDM Pro with 10+ VLANs and dual WAN
• GPU Resources: Multi-GPU setup for AI/ML workloads
• Project Management: Automated repository and development environment creation
• Service Orchestration: Network-aware container and VM deployment

🏗️ Lab Environment Understanding

The MCP server maintains deep understanding of your infrastructure:

Physical Infrastructure

• PVE1 (Proxmox): Intel i9-9900K, 64GB RAM, RTX 3070 - Container host
• PVE2 (Ubuntu): AMD 5950X, 128GB RAM, RTX 3090 - AI/ML training server
• PVE3 (Windows): Threadripper 7970X, 128GB RAM, RTX 5090+4090 - Development workstation
• UDM Pro: Network controller with dual WAN and 10+ VLANs

Network Topology

Internet
├── Starlink (Primary) - 1Gbps, 27ms latency
├── Three Mobile (Failover) - 350/100Mbps, 48ms latency  
└── UDM Pro "TheButt"
    ├── 10+ VLANs with intelligent routing
    ├── High-performance 25/50GbE backbone
    └── Network-aware service deployment

🚀 Key Features

🧠 Intelligent Infrastructure Management

• Lab Status Monitoring: Real-time comprehensive status of all nodes, services, and resources
• Performance Analysis: Bottleneck identification and optimization recommendations
• Resource Optimization: Automated resource allocation and efficiency improvements
• Capacity Planning: Predictive analysis for infrastructure scaling

🌐 Network-Aware Deployment

• Smart Network Selection: Automatically recommends optimal networks for services
• VLAN Integration: Seamless integration with 10+ pre-configured VLANs
• Performance Optimization: Network-specific optimizations for different workload types
• Security Segmentation: Intelligent isolation based on service requirements

🛠️ Automated Project Creation

• Multi-Platform Support: GitHub, GitLab, and local repository creation
• Template System: Pre-configured templates for web, AI/ML, IoT, and API projects
• Development Environments: Automated container/VM provisioning with network placement
• Best Practices: Built-in security, documentation, and development standards

📊 Infrastructure Intelligence

• Resource Analytics: Comprehensive analysis of CPU, memory, storage, and network usage
• Security Monitoring: Security posture assessment and improvement recommendations
• Cost Optimization: Identify cost-saving opportunities and efficiency improvements
• Trend Analysis: Performance trending and predictive capacity planning

🛠️ Available Tools

Lab Environment Tools

• get_lab_status - Comprehensive infrastructure status and health monitoring
• analyze_infrastructure - Performance, security, and optimization analysis
• optimize_resources - Automated resource optimization recommendations

Deployment Tools

• deploy_container - LXC container deployment with intelligent network selection
• deploy_vm - Virtual machine deployment with GPU passthrough support
• recommend_network - Network recommendation based on service requirements

Project Management Tools

• create_project - Complete project setup with repository and infrastructure
• setup_dev_environment - Automated development environment provisioning
• create_repository - GitHub/GitLab repository creation with templates

Network Management Tools

• analyze_network - Network performance and security analysis
• recommend_network - Intelligent network selection for services

Repository Management Tools

• analyze_repositories - Repository health and activity analysis
• create_repository - Multi-platform repository creation and setup

📋 Installation & Setup

Prerequisites

• Node.js 18+
• TypeScript 5.3+
• SSH access to Proxmox nodes
• GitHub token (optional, for GitHub integration)

Installation

# Clone the repository
git clone https://github.com/xRealityLabs/lab-automation-mcp.git
cd lab-automation-mcp

# Install dependencies
npm install

# Build the project
npm run build

# Configure environment
cp .env.example .env
# Edit .env with your configuration

Configuration

# Environment variables
export GITHUB_TOKEN="your_github_token"
export SSH_PRIVATE_KEY_PATH="/path/to/ssh/key"
export LOG_LEVEL="info"

# Lab configuration is built-in but can be customized

🎮 Usage Examples

Infrastructure Monitoring

// Get comprehensive lab status
await mcpServer.getLabStatus({ detailed: true })

// Analyze infrastructure performance
await mcpServer.analyzeInfrastructure({ focus: "performance" })

// Optimize resource allocation
await mcpServer.optimizeResources({ focus: "all", aggressive: false })

Network-Aware Deployment

// Deploy AI/ML container on high-performance network
await mcpServer.deployContainer({
  hostname: "ml-training",
  template: "ubuntu-22.04", 
  network: "xrealitylabs",
  memory: 16384,
  cores: 8,
  privileged: true,
  purpose: "ai-ml"
})

// Get network recommendation for IoT service
await mcpServer.recommendNetwork({
  service_type: "iot",
  requirements: ["isolation", "low-power"],
  performance_needs: "standard"
})

// Deploy crypto trading bot on secure VPN network
await mcpServer.deployContainer({
  hostname: "crypto-trader",
  template: "ubuntu-22.04",
  network: "crypto",
  memory: 4096,
  cores: 4,
  purpose: "crypto"
})

Project Automation

// Create complete AI/ML project
await mcpServer.createProject({
  name: "voice-recognition-system",
  type: "ai-ml",
  repository: "github",
  infrastructure: true,
  network: "xrealitylabs",
  technologies: ["python", "pytorch", "fastapi"]
})

// Setup development environment
await mcpServer.setupDevEnvironment({
  projectPath: "/mnt/d/repos/voice-recognition-system",
  environmentType: "container",
  network: "xrealitylabs",
  gpuAccess: true
})

🌐 Network Intelligence

Available Networks

Intelligent Network Selection

The MCP server automatically recommends optimal networks based on:

• Service Type: AI/ML → xrealitylabs, IoT → iot, Crypto → crypto, Media → mediastreamer
• Performance Requirements: High bandwidth, low latency, maximum throughput
• Security Needs: Isolation requirements, privacy concerns, compliance
• Resource Access: GPU access, storage needs, external connectivity

📁 Project Structure

lab-automation-mcp/
├── src/
│   ├── core/
│   │   └── LabEnvironment.ts         # Core lab environment intelligence
│   ├── services/
│   │   ├── ProxmoxService.ts         # Proxmox VE integration
│   │   ├── NetworkService.ts         # Network management and analysis
│   │   ├── ProjectAutomationService.ts # Project and repository automation
│   │   └── GitHubService.ts          # GitHub/GitLab integration
│   ├── utils/
│   │   └── logger.ts                 # Comprehensive logging system
│   └── index.ts                      # Main MCP server implementation
├── templates/                        # Project templates
├── dist/                            # Built JavaScript files
├── package.json                     # Dependencies and scripts
├── tsconfig.json                    # TypeScript configuration
└── README.md                        # This documentation

🔧 Development

Build & Run

# Development mode with watch
npm run dev

# Build for production
npm run build

# Start production server
npm start

# Run tests
npm test

# Lint code
npm run lint

Extending the Server

// Add new tool to the MCP server
this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
  switch (request.params.name) {
    case 'your_custom_tool':
      return await this.yourCustomHandler(request.params.arguments);
  }
});

🎯 Use Cases

AI/ML Development

• Automated Environment Setup: Create GPU-enabled containers on high-performance network
• Resource Optimization: Intelligent GPU allocation and memory management
• Data Pipeline Deployment: Network-aware data processing containers
• Model Training Infrastructure: Distributed training environment setup

IoT & Smart Home

• Secure IoT Networks: Isolated containers on dedicated IoT VLAN
• Home Assistant Integration: Automated home automation setup
• Sensor Data Processing: Edge computing containers with appropriate network placement
• Security Monitoring: Network-isolated security services

Web Development

• Full-Stack Projects: Automated frontend + backend + database deployment
• Microservices Architecture: Network-aware service deployment and discovery
• Development Environments: Containerized dev environments with proper networking
• CI/CD Integration: Automated testing and deployment pipelines

Media & Entertainment

• Media Server Setup: Plex/Jellyfin containers on dedicated media network
• Content Processing: GPU-accelerated video transcoding containers
• Storage Integration: Network-optimized storage containers
• Streaming Optimization: QoS-aware media service deployment

📊 Monitoring & Analytics

Lab Intelligence

• Real-time Monitoring: Continuous infrastructure health monitoring
• Performance Metrics: CPU, memory, storage, network, and GPU utilization
• Trend Analysis: Historical performance data and predictive analytics
• Alert System: Proactive issue detection and notification

Network Analytics

• Traffic Analysis: Per-VLAN traffic monitoring and analysis
• Performance Optimization: Bandwidth utilization and latency monitoring
• Security Monitoring: Intrusion detection and anomaly identification
• Capacity Planning: Network growth prediction and optimization

Project Analytics

• Repository Health: Code quality, activity, and maintenance status
• Development Metrics: Commit frequency, issue resolution, project velocity
• Resource Usage: Development environment resource consumption
• Cost Optimization: Infrastructure cost analysis and optimization

🔐 Security & Best Practices

Infrastructure Security

• Network Segmentation: VLAN-based isolation for different service types
• Access Control: SSH key-based authentication and RBAC
• Firewall Integration: Automated firewall rule management
• Security Monitoring: Continuous security posture assessment

Development Security

• Secret Management: Secure handling of API keys and credentials
• Code Quality: Automated security scanning and best practice enforcement
• Container Security: Secure container configuration and image management
• Audit Logging: Comprehensive logging for security and compliance

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Test thoroughly: Ensure all functionality works correctly
4. Update documentation: Include examples and use cases
5. Submit pull request: With comprehensive description

Development Guidelines

• Follow TypeScript best practices
• Implement comprehensive error handling
• Add detailed logging for debugging
• Include unit tests for new features
• Update documentation with examples

📝 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• Model Context Protocol: For the excellent MCP framework
• Proxmox Team: For robust virtualization platform
• Ubiquiti: For enterprise networking solutions
• Open Source Community: For the amazing tools and libraries

🚀 Built with ❤️ for intelligent lab automation and infrastructure orchestration!

Bringing AI-powered infrastructure management to home labs and enterprise environments.