OPNSense MCP Server

# MCP-Based IaC Platform: Executive Summary ## Vision Statement Create an AI-native Infrastructure as Code platform that uses MCP (Model Context Protocol) servers as standardized building blocks, enabling natural language infrastructure deployment across any cloud or on-premise system. ## Why This Matters ### Current IaC Limitations 1. **Steep Learning Curve**: Terraform, Pulumi, CDK require deep technical knowledge 2. **Platform Silos**: Different tools for different platforms 3. **Static Definitions**: YAML/HCL files can't adapt to runtime conditions 4. **Poor Error Handling**: Cryptic errors, manual rollback 5. **No Intelligence**: Can't learn from past deployments ### MCP-IaC Solution 1. **Natural Language**: "Deploy a web app with load balancing and SSL" 2. **Unified Interface**: One AI orchestrator for all platforms 3. **Adaptive**: AI adjusts based on current state and conditions 4. **Self-Healing**: Intelligent error recovery and rollback 5. **Learning**: Improves patterns based on successful deployments ## Architecture Overview ``` User: "Deploy CustomerPortal with high availability" ↓ ┌─────────────────────┐ │ AI Orchestrator │ │ (Claude/GPT/etc) │ └──────────┬──────────┘ │ Translates intent to deployment plan ↓ ┌─────────────────────┐ │ MCP Orchestration │ │ Layer │ └──────────┬──────────┘ │ Coordinates across MCP servers ┌──────────────┼──────────────┬──────────────┐ ↓ ↓ ↓ ↓ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │OPNSense │ │ AWS │ │ Docker │ │ K8s │ │ MCP │ │ MCP │ │ MCP │ │ MCP │ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ │ │ │ ↓ ↓ ↓ ↓ Firewall Cloud Containers Orchestration Network Resources Images Deployments LoadBalancer VMs/Storage Registry Services ``` ## Implementation Roadmap ### Phase 1: OPNSense MCP Foundation (Weeks 1-2) **Goal**: Transform basic API wrapper into resource-based IaC tool **Deliverables**: - Resource model (Firewall, Network, Services) - State management - Validation framework - Basic CRUD operations **Success Metrics**: - All resources properly modeled - State tracking functional - 100% API coverage ### Phase 2: Deployment Planning (Weeks 3-4) **Goal**: Add Terraform-like planning capabilities **Deliverables**: - State reader (current infrastructure) - Diff engine (current vs desired) - Dependency resolver - Plan visualization **Success Metrics**: - Accurate state detection - Correct change calculation - Dependency ordering works ### Phase 3: Execution Engine (Weeks 5-6) **Goal**: Build robust deployment execution with rollback **Deliverables**: - Parallel execution engine - Progress tracking - Rollback mechanisms - Checkpoint system **Success Metrics**: - Reliable deployments - Automatic rollback on failure - Real-time progress updates ### Phase 4: Multi-MCP Integration (Weeks 7-8) **Goal**: Enable cross-platform deployments **Deliverables**: - Cross-MCP references - Unified orchestrator - Deployment patterns - Policy engine **Success Metrics**: - Deploy across 2+ platforms - Reference resolution works - Patterns simplify deployment ## Use Cases ### 1. Web Application Deployment **Natural Language**: "Deploy our customer portal with load balancing, SSL, and auto-scaling" **AI Actions**: 1. Create isolated network (OPNSense VLAN) 2. Configure firewall rules (OPNSense) 3. Provision servers (AWS EC2) 4. Build container (Docker) 5. Setup load balancer (OPNSense HAProxy) 6. Configure SSL (Let's Encrypt) 7. Setup monitoring (Prometheus) ### 2. Disaster Recovery **Natural Language**: "Failover the payment system to backup datacenter" **AI Actions**: 1. Check health of primary 2. Update DNS (OPNSense) 3. Redirect traffic (HAProxy) 4. Scale up backup servers 5. Verify data sync 6. Update monitoring ### 3. Development Environment **Natural Language**: "Spin up a dev environment for the mobile team" **AI Actions**: 1. Clone production config 2. Create isolated network 3. Reduce resource sizes 4. Deploy test data 5. Configure access rules 6. Set expiration ## Competitive Advantages ### vs Terraform - **Natural language** instead of HCL - **Intelligent planning** vs static plans - **Self-healing** vs manual intervention - **Cross-platform native** vs provider silos ### vs Pulumi - **No coding required** vs TypeScript/Python - **AI-driven** vs programmatic - **Pattern library** vs writing from scratch - **Policy enforcement** built-in ### vs AWS CDK - **Multi-cloud** vs AWS-only - **Natural language** vs TypeScript - **No compilation** required - **Instant feedback** from AI ## Technical Differentiators 1. **MCP Protocol**: Standardized interface for all infrastructure 2. **Resource Model**: Consistent across all platforms 3. **State Management**: Distributed but coordinated 4. **Dependency Resolution**: Cross-platform aware 5. **Policy Engine**: Compliance built-in ## Business Value ### For Developers - 10x faster deployment - No infrastructure expertise needed - Focus on application logic - Automatic best practices ### For Operations - Consistent deployments - Automatic documentation - Compliance enforcement - Reduced errors ### For Business - Faster time to market - Reduced infrastructure costs - Improved reliability - Better security posture ## Success Metrics ### Phase 1 Success - ✅ 20+ resource types modeled - ✅ State persistence working - ✅ All CRUD operations functional ### Phase 2 Success - ✅ Accurate diff generation - ✅ Dependency ordering correct - ✅ Human-readable plans ### Phase 3 Success - ✅ 99% deployment success rate - ✅ Automatic rollback working - ✅ <5min deployment time ### Phase 4 Success - ✅ 3+ MCP servers integrated - ✅ Cross-platform deployments work - ✅ 5+ patterns implemented ## Next Steps ### Immediate (This Week) 1. Complete Phase 1 resource modeling 2. Test state management 3. Document API coverage ### Short Term (This Month) 1. Implement planning engine 2. Build execution engine 3. Create first patterns ### Medium Term (This Quarter) 1. Add AWS MCP server 2. Add Docker MCP server 3. Build pattern library 4. Launch beta ### Long Term (This Year) 1. 10+ MCP servers 2. 100+ patterns 3. Visual designer 4. SaaS offering ## Call to Action This platform represents the future of infrastructure management: - **AI-native** from the ground up - **Universal** across all platforms - **Intelligent** and self-improving - **Accessible** to all skill levels The OPNSense MCP server is just the beginning. With this foundation, we're building the infrastructure platform that will power the next generation of applications. **Let's make infrastructure as easy as having a conversation.**