The model context protocol (MCP) server for Kubernetes is moving toward technology preview (TP), and it’s bringing a powerhouse integration with it: the Kiali toolset. By integrating Kiali into the MCP server, we are bridging the gap between large language models (LLM) and your service mesh. This means your AI assistant doesn't just "talk" about your cluster, it can now visualize traffic, diagnose latency, and manage Istio configurations using the same trusted logic that powers the Kiali UI.Why Kiali in MCP?While standard Kubernetes tools handle pods and services, the Kiali toolset provides mesh-awareness. It understands the "connect, secure, and observe" philosophy of Istio. Whether you are debugging a 503 error or mapping cross-namespace dependencies, these tools allow an LLM to act as a specialized service mesh engineer.The Kiali toolset at a glanceThe following tools are now available for use within the MCP server, allowing for deep introspection of your mesh:mesh_status: High-level health check of Istio, Kiali, and the control planetraffic_graph: Visualize service-to-service dependencies and mTLS statusistio_config_read/write: List, get, create, or patch Istio objects (VirtualServices, for example)Resource_details: Get details about specific Kubernetes and Istio resource manifeststrace_list/details: Pull Jaeger and Tempo distributed traces for request-level debuggingpod_performance: Summarize CPU and memory usage compared to actual Kubernetes requests and limitslogs: Fetch container logs with built-in severity (ERROR or WARN, for example) filteringmetrics: See traffic trends, throughput, and latency quantiles (p95, p99).How to get startedThe Kiali MCP integration is a modernized approach to mesh management. To use these features, your environment must meet one of the following version requirements: Red Hat OpenShift Service Mesh: Requires v3.3.3 or higherKiali: Requires Kiali v2.25 or higherIf your current Kiali version is below v2.25, you can test the latest capabilities by using the Kiali operator or Helm to deploy the specific image.Which method should I use?Use the operator method if you are on a standard Red Hat OpenShift installation where the Kiali operator manages the lifecycle and CRDs automatically.Use the Helm method if you prefer manual version control or are integrating Kiali into a CI/CD GitOps pipeline (like ArgoCD).Operator method1. Enable ad-hoc imagesFirst, allow the Kiali operator to use non-default images:oc set env deploy/kiali-operator \