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The Model Context Protocol (MCP) is an open standard for AI model communication.
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damens_mcps

GitHub

A collection of MCP servers I've put together with various AI tools for infrastructure management

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3

Damen's MCP Servers Collection 🚀

License: MIT
GitHub Issues
GitHub Stars
Test Status
Code Quality
codecov
Code style: black
Imports: isort
security: bandit
pre-commit

Note: These MCP servers were generated using Cursor AI tools. While functional, they may benefit from human review and refinement for production use.

A comprehensive collection of Model Context Protocol (MCP) servers for managing various infrastructure components. These servers enable AI assistants to directly interact with and manage your infrastructure through standardized MCP interfaces.

📋 Project Overview

This repository contains MCP servers for the following platforms:

ProjectStatusDescription
pfSense MCP✅ Production ReadyFirewall and network management for pfSense
TrueNAS MCP✅ Production ReadyStorage and NAS management for TrueNAS
iDRAC MCP✅ Production ReadyDell PowerEdge server management via iDRAC
Proxmox MCP✅ Production ReadyVirtualization platform management for Proxmox VE

🚀 Quick Start

Prerequisites

  • Python 3.8+ for all projects
  • Docker (optional, for containerized deployment)
  • Network access to your target systems
  • API credentials for each platform

Installation & Setup

  1. Clone the repository:
    git clone <your-repo-url>
    cd damens_mcps
  2. Choose your project and navigate to it:
    cd pfsense-mcp    # For pfSense management
    cd truenas-mcp    # For TrueNAS management
    cd idrac-mcp      # For Dell server management (production ready)
    cd proxmox-mcp    # For Proxmox management (production ready)
  3. Set up virtual environment:
    python3 -m venv .venv
    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  4. Configure environment:
    cp env.example .env
    # Edit .env with your system credentials
  5. Start the server:
    # Method varies by project - check individual READMEs
    python -m src.http_server
    # or
    python -m src.cli serve

🔧 Configuration

Environment Variables

Each project uses environment variables for configuration. Copy the env.example file to .env and update with your settings:

# Example for pfSense
PFSENSE_HOST=192.168.1.1
PFSENSE_USERNAME=admin
PFSENSE_PASSWORD=your-password
PFSENSE_API_KEY=your-api-key

# Example for TrueNAS
TRUENAS_HOST=192.168.1.100
TRUENAS_API_KEY=your-api-key
TRUENAS_USERNAME=admin
TRUENAS_PASSWORD=your-password

Security Notes

⚠️ Important Security Considerations:

  • Change default passwords in production
  • Use API keys instead of passwords when possible
  • Update IP addresses from example values
  • Use HTTPS for all connections
  • Restrict network access to MCP servers

🐳 Docker Deployment

Docker deployment is available for TrueNAS MCP and Proxmox MCP projects:

# Navigate to project with Docker support
cd truenas-mcp  # or proxmox-mcp

# Build and run with Docker Compose
docker-compose -f docker/docker-compose.yml up -d

# Or build manually
docker build -f docker/Dockerfile -t mcp-server .
docker run -p 8000:8000 --env-file .env mcp-server

Docker Support by Project:

ProjectDocker SupportK8s Support
TrueNAS MCP✅ Yes✅ Yes
Proxmox MCP✅ Yes✅ Yes
pfSense MCP❌ No❌ No
iDRAC MCP❌ No❌ No

Note: pfSense and iDRAC projects are designed for direct Python execution due to their specific deployment requirements and are not currently containerized.

☸️ Kubernetes Deployment

Kubernetes manifests are provided for TrueNAS MCP and Proxmox MCP:

# Navigate to project with K8s support
cd truenas-mcp  # or proxmox-mcp

# Deploy to Kubernetes
kubectl apply -f k8s/

# Check deployment status
kubectl get pods -l app=mcp-server

📊 Monitoring & Health Checks

Each server provides health check endpoints:

# Check server health
curl http://localhost:8000/health

# Get server status
curl http://localhost:8000/status

🔍 Testing

Test Coverage Status

All projects now have comprehensive test infrastructure with CI/CD automation:

ProjectTestsCoverageStatus
pfSense14+6% (49% validation)✅ Passing
TrueNAS20+Framework ready✅ Ready
iDRAC11+15% (91% validation)✅ Passing
Proxmox18+11% (72% validation)✅ Passing

Running Tests

# Navigate to any project
cd <project-name>

# Install test dependencies
pip install pytest pytest-cov pytest-asyncio pytest-mock

# Run all tests
pytest tests/ -v

# Run with coverage report
pytest tests/ --cov=src --cov-report=html

# Run specific test file
pytest tests/test_validation.py -v

Code Quality Checks

Run code quality checks locally on all projects:

# Run all quality checks (black, isort, flake8, mypy, bandit, safety, interrogate)
./run_code_quality_checks.sh

See CODE_QUALITY.md for detailed code quality standards and tools.

CI/CD

Automated checks run on every push and pull request via GitHub Actions:

  • ✅ All 4 projects tested independently
  • ✅ Coverage reports uploaded to Codecov
  • ✅ Python 3.12 compatibility verified
  • ✅ Code quality checks (linting, formatting, security, type checking)

See TESTING.md for detailed testing guide and TEST_COVERAGE_SUMMARY.md for coverage details.

Integration Testing

# Test with actual system (requires configuration)
python examples/basic_usage.py

📚 Documentation

Each project has detailed documentation:

  • Individual READMEs in each project directory
  • CLAUDE.md - Development guidelines and architecture overview for AI assistant integration
  • CODE_QUALITY.md - Code quality standards and tools documentation
  • API Documentation in docs/ folders
  • Example scripts in examples/ directories
  • Deployment guides for Docker and Kubernetes

🛠️ Development

Project Structure

project-name/
├── src/                    # Source code
│   ├── auth.py            # Authentication
│   ├── client.py          # API client
│   ├── server.py          # MCP server
│   └── utils/             # Utilities
├── tests/                 # Test files
├── examples/              # Example usage
├── docker/                # Docker files
├── k8s/                   # Kubernetes manifests
├── docs/                  # Documentation
├── requirements.txt       # Python dependencies
├── env.example           # Environment template
└── README.md             # Project documentation

Adding New Features

  1. Update source code in src/
  2. Add tests in tests/
  3. Update documentation in docs/
  4. Test thoroughly before deployment

🚨 Important Notes

✅ Production Ready Projects

All MCP servers in this collection are now production ready and have been thoroughly tested:

  • pfSense MCP - Firewall and network management
  • TrueNAS MCP - Storage and NAS management
  • iDRAC MCP - Dell PowerEdge server management
  • Proxmox MCP - Virtualization platform management

These servers have been tested with real systems and include comprehensive error handling, input validation, and security features.

🔒 Security

  • Never commit .env files or credentials
  • Use strong passwords and API keys
  • Restrict network access appropriately
  • Monitor logs for suspicious activity
  • Keep dependencies updated

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests for new functionality
  5. Update documentation
  6. Submit a pull request

📄 License

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

🆘 Support

  • Issues: Create GitHub issues for bugs or feature requests
  • Documentation: Check individual project READMEs
  • Examples: Review examples/ directories for usage patterns

🔗 Related Links

  • Model Context Protocol
  • pfSense Documentation
  • TrueNAS Documentation
  • Dell iDRAC Documentation
  • Proxmox VE Documentation

Happy Infrastructure Management! 🎉

Repository

FI
filthyrake

filthyrake/damens_mcps

Created

August 9, 2025

Updated

April 13, 2026

Language

Python

Category

AI