AI Agent for K8s Multi-Cluster MCP
Seamlessly manage and automate operations across multiple Kubernetes clusters with the Multi Cluster Kubernetes MCP Server integration. Standardize your Kubernetes management with powerful AI-driven context switching, cross-cluster operations, rollout management, and diagnostics—all from a single interface. Unlock centralized multi-cluster control, instant insights, and rapid troubleshooting for dev, staging, and production environments.
Centralized Multi-Cluster Kubernetes Management
Effortlessly control multiple Kubernetes clusters from one AI-powered platform. Instantly list, compare, and manage resources across all your clusters using multiple kubeconfig files. Context switching, resource inspection, and cross-cluster operations are just a command away, ensuring complete visibility and fast troubleshooting for all your Kubernetes environments.
- Unified Cluster Access.
- Manage all Kubernetes clusters using multiple kubeconfig files for streamlined access and operations.
- AI-Powered Context Switching.
- Instantly switch between dev, staging, and production clusters without manual reconfiguration.
- Cross-Cluster Insights.
- Compare resources, status, and configurations across clusters for faster decision-making.
- Centralized Resource Management.
- View and control all namespaces, nodes, and resources from a single interface.
Comprehensive Rollout & Resource Control
Take command of your Kubernetes deployments with advanced rollout management and resource controls. Monitor rollout status, undo or restart rollouts, and adjust resource limits in real time. Effortlessly scale, pause, resume, and update workloads, ensuring your applications are always optimized and resilient.
- Automated Rollout Management.
- Monitor status, view history, and control rollouts with undo, restart, pause, and resume actions.
- Resource Scaling & Autoscaling.
- Scale deployments and configure Horizontal Pod Autoscalers directly from the interface.
- Live Resource Updates.
- Update CPU/memory limits and requests, ensuring optimal application performance.
Diagnostics, Monitoring & Intelligent Operations
Diagnose application issues, monitor resource usage, and perform advanced operations using built-in AI tools. Instantly retrieve pod logs, execute commands in containers, and receive actionable diagnostics to keep your Kubernetes workloads healthy and performant.
- Instant Diagnostics.
- Diagnose application issues, retrieve events, and review logs with AI-driven insights.
- Live Pod Operations.
- Execute commands in pods, get logs, and manage workloads effortlessly.
- Real-Time Metrics & Monitoring.
- Monitor CPU/memory usage for nodes and pods to ensure optimal resource allocation.
MCP INTEGRATION
Available Kubernetes MCP Integration Tools
The following tools are available as part of the Kubernetes MCP integration:
- k8s_get_contexts
List all available Kubernetes contexts across your configured clusters.
- k8s_get_namespaces
List all namespaces in a specified Kubernetes context.
- k8s_get_nodes
List all nodes in a Kubernetes cluster for infrastructure visibility.
- k8s_get_resources
List resources of a specified kind, such as pods, deployments, or services.
- k8s_get_resource
Retrieve detailed information about a specific Kubernetes resource.
- k8s_get_pod_logs
Fetch logs from a specific pod for monitoring and troubleshooting.
- k8s_describe
Show detailed, describe-style information about Kubernetes resources.
- k8s_apis
List all available APIs in the connected Kubernetes cluster.
- k8s_crds
List all Custom Resource Definitions (CRDs) in the cluster.
- k8s_top_nodes
Display resource usage statistics (CPU/memory) for cluster nodes.
- k8s_top_pods
Display resource usage (CPU/memory) of pods in the cluster.
- k8s_diagnose_application
Diagnose issues with a deployment or application in your cluster.
- k8s_rollout_status
Get the current status of a Kubernetes resource rollout.
- k8s_rollout_history
Retrieve the revision history of a resource rollout.
- k8s_rollout_undo
Undo a rollout to a previous revision for rapid rollback.
- k8s_rollout_restart
Restart a rollout to redeploy workloads with new configurations.
- k8s_rollout_pause
Pause an ongoing rollout operation for safe intervention.
- k8s_rollout_resume
Resume a previously paused rollout operation.
- k8s_create_resource
Create a new Kubernetes resource using YAML or JSON definitions.
- k8s_apply_resource
Apply configuration to create or update a Kubernetes resource.
- k8s_patch_resource
Patch and update fields of an existing resource.
- k8s_label_resource
Add or update labels on a specified Kubernetes resource.
- k8s_annotate_resource
Add or update annotations on a resource for metadata management.
- k8s_scale_resource
Scale a resource, such as a deployment, to the desired replica count.
- k8s_autoscale_resource
Configure a Horizontal Pod Autoscaler for dynamic scaling.
- k8s_update_resources
Update resource requests and limits for deployments and containers.
- k8s_expose_resource
Expose a Kubernetes resource as a new service.
- k8s_set_resources_for_container
Set CPU and memory limits or requests for specific containers.
- k8s_cordon_node
Mark a node as unschedulable to prepare for maintenance.
- k8s_uncordon_node
Mark a node as schedulable after maintenance is completed.
- k8s_drain_node
Drain a node by evicting pods in preparation for maintenance.
- k8s_taint_node
Add taints to a node to control pod scheduling.
- k8s_untaint_node
Remove taints from a node to restore normal scheduling.
- k8s_pod_exec
Execute a command inside a pod's container for troubleshooting or administration.
Connect Your Kubernetes Multi-Cluster with FlowHunt AI
Connect your Kubernetes Multi-Cluster to a FlowHunt AI Agent. Book a personalized demo or try FlowHunt free today!
What is Multicluster MCP Server
The Multicluster MCP Server is a robust gateway designed to enable Generative AI (GenAI) systems to interact seamlessly with multiple Kubernetes clusters via the Model Context Protocol (MCP). This server empowers organizations to comprehensively operate, observe, and manage Kubernetes resources across numerous clusters from a centralized interface. With full support for kubectl, the Multicluster MCP Server streamlines workflows for deploying, scaling, and monitoring applications in multi-cluster environments, making it an essential tool for teams running distributed AI workloads or needing unified cluster management. The open-source nature of the server ensures it is both accessible and adaptable for developer and enterprise needs.
Capabilities
What we can do with Multicluster MCP Server
With the Multicluster MCP Server, users and AI systems can efficiently manage, observe, and automate operations across multiple Kubernetes clusters. The platform provides a unified gateway, enabling advanced deployment strategies, comprehensive monitoring, and seamless integration for GenAI-powered applications.
- Unified Cluster Management
- Centrally operate and manage resources across several Kubernetes clusters.
- Full kubectl Integration
- Perform advanced cluster operations using familiar kubectl commands and workflows.
- Observability & Metrics
- Retrieve, analyze, and visualize metrics, logs, and alerts from all connected clusters.
- GenAI Workflow Automation
- Streamline operations for Generative AI applications across distributed environments.
- Open-source & Extensible
- Free to use and easily extendable for custom enterprise or developer needs.
How AI Agents Benefit from Multicluster MCP Server
AI agents leveraging the Multicluster MCP Server gain unified access to multiple Kubernetes clusters, enabling them to automate complex deployment and scaling tasks, monitor application health, and orchestrate distributed AI workflows efficiently. This reduces operational complexity, enhances resource utilization, and accelerates the deployment of intelligent applications across multi-cloud and hybrid environments.