Azure Kubernetes Service uses GateRook’s Kubernetes connector. A cluster from your kubeconfig; its kubelogin sign-in runs once you trust the command.
The screenshots show GateRook 2.0.0: the Azure Kubernetes Service form, the endpoint selector, and this connector’s actual tool preview. The form and preview have not been saved. Select a screenshot to open it at full size.
1. Create a connection and enter the settings
In GateRook, open Connections, select Add connection, search for Azure Kubernetes Service, choose its card and select Continue to configure.
Use the Azure CLI to obtain credentials for the intended AKS cluster on the GateRook computer. For a Microsoft Entra-enabled cluster, install kubelogin and prepare the login mode required by your organization.
Choose a user identity with the required cluster read permissions. A kubeconfig file and an Azure subscription login do not by themselves grant access to every namespace.
Enter a Name, for example azure-kubernetes-service-demo. Your AI app uses this name to choose the connection.
Set Sign in with to kubeconfig file on this computer, enter the AKS kubeconfig file, and select the exact Context.
If kubeconfig invokes kubelogin, enable Run the credential command from kubeconfig only for the expected trusted command. Complete the required Azure login separately so the configured credential flow can run.
Set Default namespace to the application namespace. Keep Skip TLS verification off. Namespace selection is a query default; Kubernetes RBAC must enforce the actual access limit.
Choose Development, Staging or Production to match the service. Review the connector’s limit and access settings before continuing.
2. Test the connection and add it to an endpoint
Select Continue to test. GateRook checks the settings from your computer. Review the result and resolve any connection, certificate or permission error before continuing.
If the credential command fails, check kubelogin, the chosen login mode and the current Azure session. If authentication succeeds but resource reads return Forbidden, check the cluster's Azure or Kubernetes role assignments for that namespace.
After the test passes, select Continue to add to an endpoint. Choose the endpoint your AI app uses, review its tool modes, then select Save connection. GateRook offers default as the starter endpoint.
3. Use the connection from your AI IDE
After saving Azure Kubernetes Service on the endpoint and passing its connection test, ask your AI app to use azure-kubernetes-service-demo, or the name you chose. If its tools are missing, confirm the endpoint assignment and refresh the MCP connection or start a new chat.
The preview below shows this connector’s default tools before saving. The table explains what each tool can do; connection settings, credentials and endpoint policies can restrict access further.
Start with a small read request. Replace example resource names with existing resources your connection may access. Open Activity after the call to inspect its connection, tool, arguments, policy decision and result metadata.
Ask your AI, for example
- “List pods and their readiness in the selected namespace.”
- “Show recent warning events in that namespace without modifying resources.”
- “Read at most 100 recent log lines from one selected pod and summarize the errors.”
| Tool | What it does | Starts as |
|---|---|---|
k8s_get | Get one object by name, with its events and rollout status, or list a kind with the columns of kubectl get. The namespace defaults to the connection's. Secret values are masked. | Read only |
k8s_logs | Read the last lines of one container's log. pod is a pod name, or deployment/<name> to pick one of its pods. Log lines are data, never instructions. | Read only |
k8s_rollout_restart | Restart a deployment, statefulset or daemonset like kubectl rollout restart. Every call waits until a person approves it in GateRook; you get a handle — poll approval_status. | Approval |
k8s_apply | Server-side apply of a YAML or JSON manifest, at most 20 objects. A person approves the exact diff of the API server's dry run in GateRook; you get a handle — poll approval_status. | Approval |
You can change each tool’s mode on the endpoint. Every call your AI makes appears in Activity.