Quick Start

Get started with DevOps AI Toolkit in minutes - deploy applications, manage policies, and remediate issues using AI-powered Kubernetes workflows through MCP.

For the easiest setup, we recommend installing the complete dot-ai stack which includes all components pre-configured. See the Stack Installation Guide.

Continue below if you want to install components individually (for non-Kubernetes setups or granular control over configuration).

Overview

What it does: DevOps AI Toolkit provides AI-powered Kubernetes deployment, remediation, policy management, and capability discovery through conversational workflows in your MCP-enabled coding agent.

Use when: You want intelligent Kubernetes operations without memorizing commands, need AI-powered troubleshooting, or want to establish governance policies across your cluster.

📖 Full Guide: See MCP Setup Guide for detailed configuration options and Tools Overview for complete feature reference.

Prerequisites

Works without AI keys:

• ✅ Shared prompts library - No API key needed, works with any MCP-enabled coding agent

For AI-powered features (deployment, remediation, patterns, policies, capabilities):

• AI Model API key - Required for AI analysis and intelligent recommendations
  • Multiple AI models supported - see AI Model Configuration for all options and setup
  • Quick setup: Claude (default) - export ANTHROPIC_API_KEY=your_key_here

For Kubernetes deployment recommendations:

• kubectl configured with cluster access
  • Verify cluster access with: kubectl get nodes
  • Should show your cluster nodes without authentication errors

For organizational pattern management:

• Vector DB service (Qdrant) for pattern storage and semantic search
• Embedding provider API key - Required for semantic pattern matching:
  • OpenAI: OPENAI_API_KEY
  • Google: GOOGLE_API_KEY
  • Amazon Bedrock: AWS credentials via environment variables or ~/.aws/credentials

For policy management and governance:

• Vector DB service (Qdrant) for policy storage and semantic search
• Embedding provider API key - Required for semantic policy matching (same options as above)
• Optional: Kyverno installed in cluster for active policy enforcement

Installation

DevOps AI Toolkit is designed to be used through AI development tools via MCP (Model Context Protocol). No direct installation needed - simply configure your AI tool to connect to the MCP server.

Usage

🎯 Recommended: Kubernetes Setup (Full Features) Production-ready deployment with autonomous capability scanning via controller:

Step 0: Create a Kubernetes Cluster (Optional)

Skip this step if you already have a Kubernetes cluster with an ingress controller.

Prerequisites: Install Kind if you don't have it.

Create a Kind cluster with ingress support:

# Create Kind cluster configuration
cat > kind-config.yaml << 'EOF'
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
- role: control-plane
  extraPortMappings:
  - containerPort: 80
    hostPort: 80
    protocol: TCP
  - containerPort: 443
    hostPort: 443
    protocol: TCP
EOF

# Create the cluster
kind create cluster --name dot-ai --config kind-config.yaml

# Install nginx ingress controller for Kind
kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/main/deploy/static/provider/kind/deploy.yaml

# Wait for ingress controller to be ready
kubectl wait --namespace ingress-nginx \
  --for=condition=ready pod \
  --selector=app.kubernetes.io/component=controller \
  --timeout=90s

Step 1: Set Environment Variables

export ANTHROPIC_API_KEY="sk-ant-api03-your-key-here"
export OPENAI_API_KEY="sk-proj-your-openai-key-here"
export DOT_AI_AUTH_TOKEN=$(openssl rand -base64 32)

# Ingress class - change to match your ingress controller (traefik, haproxy, etc.)
export INGRESS_CLASS_NAME="nginx"

Step 2: Install via Helm

# Set versions from GitHub packages
export DOT_AI_VERSION="..."  # https://github.com/vfarcic/dot-ai/pkgs/container/dot-ai%2Fcharts%2Fdot-ai
export DOT_AI_CONTROLLER_VERSION="..."  # https://github.com/vfarcic/dot-ai-controller/pkgs/container/dot-ai-controller%2Fcharts%2Fdot-ai-controller

# Install controller (enables autonomous capability scanning)
helm install dot-ai-controller \
  oci://ghcr.io/vfarcic/dot-ai-controller/charts/dot-ai-controller:$DOT_AI_CONTROLLER_VERSION \
  --namespace dot-ai --create-namespace --wait

# Install MCP server
helm install dot-ai-mcp oci://ghcr.io/vfarcic/dot-ai/charts/dot-ai:$DOT_AI_VERSION \
  --set secrets.anthropic.apiKey="$ANTHROPIC_API_KEY" \
  --set secrets.openai.apiKey="$OPENAI_API_KEY" \
  --set secrets.auth.token="$DOT_AI_AUTH_TOKEN" \
  --set ingress.enabled=true \
  --set ingress.className="$INGRESS_CLASS_NAME" \
  --set ingress.host="dot-ai.127.0.0.1.nip.io" \
  --set controller.enabled=true \
  --namespace dot-ai --wait

Step 3: Create MCP Configuration

Create the MCP client configuration file with your auth token:

cat > .mcp.json << EOF
{
  "mcpServers": {
    "dot-ai": {
      "type": "http",
      "url": "http://dot-ai.127.0.0.1.nip.io",
      "headers": {
        "Authorization": "Bearer $DOT_AI_AUTH_TOKEN"
      }
    }
  }
}
EOF

Note: The $DOT_AI_AUTH_TOKEN variable is expanded when creating the file. Make sure you're in the same terminal session where you set the environment variables in Step 1.

Step 4: Start Your MCP Client

claude  # or your preferred MCP-enabled AI tool

Verify everything works by asking:

Show dot-ai status

You should see a status report showing all components are healthy.

What you get:

• ✅ Full Features: All capabilities including autonomous scanning via controller
• ✅ Production-Ready: Scalable deployment with proper resource management
• ✅ Automatic Capability Discovery: Controller watches for CRD changes and scans automatically
• ✅ Team Collaboration: Shared MCP server accessible by multiple developers

Full Configuration: See the MCP Setup Guide for advanced configuration options.

Step 5: Start Using Conversational Workflows

Try these example prompts to explore the toolkit:

What You Want	Example Prompt	Guide
Scan capabilities	Use controller (recommended) or "Scan my cluster for capabilities"	Capability Management
Query cluster	"What databases are running?"	Cluster Query
Deploy an app	"I want to deploy a web application"	Recommendation Guide
Operate resources	"Scale my database to 3 replicas"	Operations Guide
Fix issues	"Something is wrong with my database"	Remediation Guide
Create patterns	"Create a pattern for database deployments"	Pattern Management
Create policies	"Create a policy requiring resource limits"	Policy Management
Setup project	"Help me setup governance files"	Project Setup Guide
Use prompts	/dot-ai:prd-create	Prompts Guide

Next Steps

📖 MCP Setup Guide → - Detailed configuration, troubleshooting, and examples

📖 Complete Tools & Features Reference → - Comprehensive guide to all available tools, workflows, and advanced features