DevOps AI Toolkit

# ToolHive Kubernetes Setup Guide **Deploy DevOps AI Toolkit MCP Server to Kubernetes using ToolHive operator - simplified enterprise deployment with built-in security and multi-tenancy.** ## When to Use This Method ✅ **Perfect for:** - Enterprise environments requiring built-in security - Multi-team deployments with isolation requirements - Organizations wanting simplified Kubernetes management - Teams preferring operator-managed deployments - Environments requiring automatic RBAC and security policies ❌ **Consider alternatives for:** - Single developer local usage (use [Docker setup](docker-setup.md) instead) - Teams wanting full control over Kubernetes resources (use [Traditional Kubernetes](kubernetes-setup.md) instead) - Environments without operator installation permissions → See [other setup methods](../mcp-setup.md#setup-methods) for alternatives → **Learn more about ToolHive**: See [ToolHive documentation](https://docs.stacklok.com/toolhive) for operator details and advanced configuration ## What You Get - **HTTP Transport MCP Server** - Direct HTTP/SSE access for MCP clients - **ToolHive Operator Management** - Simplified deployment via MCPServer custom resource - **Built-in Security** - Automatic RBAC, security policies, and container isolation - **Multi-tenancy Support** - Built-in isolation between different MCP server instances - **Integrated Qdrant Database** - Vector database for capability and pattern management - **Native HTTP Support** - Direct MCP-over-HTTP without proxy translation ## Prerequisites - Kubernetes cluster (1.19+) with kubectl access - Helm 3.x installed - Cluster admin permissions (required for ToolHive operator installation) - AI model API key (default: Anthropic). See [AI Model Configuration](../mcp-setup.md#ai-model-configuration) for available model options. - OpenAI API key (required for vector operations) ## Quick Start (10 Minutes) ### Step 1: Install ToolHive Operator Install the ToolHive operator CRDs and operator: ```bash # Install ToolHive operator CRDs helm upgrade --install toolhive-operator-crds \ oci://ghcr.io/stacklok/toolhive/toolhive-operator-crds \ --wait # Install ToolHive operator helm upgrade --install toolhive-operator \ oci://ghcr.io/stacklok/toolhive/toolhive-operator \ --namespace toolhive-system \ --create-namespace \ --wait ``` ### Step 2: Set Environment Variables Export your API keys: ```bash # Required: Set your API keys export ANTHROPIC_API_KEY="sk-ant-api03-..." export OPENAI_API_KEY="sk-proj-..." # Optional: Custom endpoints (OpenRouter, self-hosted) # See: https://github.com/vfarcic/dot-ai/blob/main/docs/mcp-setup.md#custom-endpoint-configuration export CUSTOM_LLM_API_KEY="sk-or-v1-..." export CUSTOM_LLM_BASE_URL="https://openrouter.ai/api/v1" ``` ### Step 3: Install the Helm Chart with ToolHive Method Install the MCP server using ToolHive deployment method: ```bash # Install using ToolHive deployment method helm install dot-ai-mcp oci://ghcr.io/vfarcic/dot-ai/charts/dot-ai:0.83.0 \ --set deployment.method=toolhive \ --set secrets.anthropic.apiKey="$ANTHROPIC_API_KEY" \ --set secrets.openai.apiKey="$OPENAI_API_KEY" \ --set ingress.enabled=true \ --set ingress.host="dot-ai.127.0.0.1.nip.io" \ --create-namespace \ --namespace dot-ai \ --wait ``` **Notes**: - This documentation may use an outdated version. Check the [GitHub Releases](https://github.com/vfarcic/dot-ai/releases) for the latest version and replace `0.83.0` with the current version tag. - Replace `dot-ai.127.0.0.1.nip.io` with your desired hostname for external access. - For enhanced security, create a secret named `dot-ai-secrets` with keys `anthropic-api-key` and `openai-api-key` instead of using `--set` arguments. - For all available configuration options, see the [Helm values file](https://github.com/vfarcic/dot-ai/blob/main/charts/values.yaml). - **Observability/Tracing**: Add tracing environment variables via `extraEnv` in your values file. See [Observability Guide](../observability-guide.md) for complete configuration. ### Step 4: Configure MCP Client Create an `.mcp.json` file in your project root: ```json { "mcpServers": { "dot-ai": { "type": "http", "url": "http://dot-ai.127.0.0.1.nip.io" } } } ``` **Save this configuration:** - **Claude Code**: Save as `.mcp.json` in your project directory - **Other clients**: See [MCP client configuration](../mcp-setup.md#mcp-client-compatibility) for filename and location **Notes**: - Replace the URL with your actual hostname if you changed `ingress.host`. - For production deployments, configure TLS certificates and use `https://` URLs for secure connections. ### Step 5: Start Your MCP Client Start your MCP client (e.g., `claude` for Claude Code). The client will automatically connect to your ToolHive-deployed MCP server. ### Step 6: Verify Everything Works In your MCP client, ask: ``` Show dot-ai status ``` You should see comprehensive system status including Kubernetes connectivity, vector database, and all available features.