"Kubernetes" matching MCP servers:

Manages Kubernetes clusters through kubectl and Helm operations including pod cleanup, node management, and troubleshooting.

TypeScript implementation of Kubernetes cluster operations for pods, deployments, services.

Provides access to Kubernetes troubleshooting runbooks, enabling search, discovery, and retrieval of guides for common issues like pod crashes, node problems, and network failures.

Enables diagnosing Kubernetes clusters without creating or changing cluster objects, with tools to inspect health, workloads, events, storage, RBAC, custom resources, logs, and configuration.

Enables safe, read-only interaction with Kubernetes clusters, allowing users to list resources and fetch logs without any create/update/delete operations.

Enables managing multiple Kubernetes clusters simultaneously with over 60 tools for cluster operations, resource management, monitoring, RBAC, storage, networking, and more through AI assistants.

Gives AI assistants read-only access to a Kubernetes cluster's observability stack — Loki, Prometheus, Tempo, the Kubernetes API — plus AWS telemetry from CloudTrail, Config, ECR, and RDS. 23 tools for querying logs, metrics, traces, Kubernetes events, deployments, and audit history in plain language.

Read-only MCP server for safe Kubernetes inspection, diagnosis, and debugging. Supports Kubernetes core, Helm, Argo Workflows, and Argo CD.

A lightweight, centralized control plane server that enables management of multiple Kubernetes clusters simultaneously, supporting context switching and CRUD operations on common Kubernetes resources.

Analyzes git repositories to extract installation requirements and validates feasibility against OpenShift/Kubernetes clusters.

Model Context Protocol (MCP) server for debugging, analyzing, and diagnosing Kubernetes clusters directly from AI agents.

VMware AVI Load Balancer (NSX Advanced Load Balancer) management plus AKO Kubernetes integration. 29 MCP tools across virtual services, pools, analytics, and AKO lifecycle, with double-confirmation gates and audit logging for destructive operations.

Enables AI agents to audit Kubernetes manifests for configuration traps that pass linters but silently break production, via read-only tools to audit YAML, list known traps, and explain trap details.

Enables agents to autonomously investigate deployment health by inspecting Kubernetes resources, comparing live cluster state against Git, and correlating findings with Dynatrace problems.

An MCP server that enables natural language querying and troubleshooting of Kubernetes clusters, supporting multi-cluster switching and providing tools for pods, nodes, events, and logs.

Exposes a Kubernetes cluster to MCP-compatible AI clients, enabling read-only and optional write operations on cluster resources like pods, deployments, and namespaces through natural language.

A dynamic MCP proxy that allows switching environment variables on-the-fly, enabling context switching for multiple AWS accounts or Kubernetes clusters without restarting the client.

A Model Context Protocol (MCP) server that interfaces with the Apache YuniKorn Scheduler, allowing AI agents to observe and reason about Kubernetes batch workload resource management, queue hierarchies, and application states.

Enables LLMs to migrate Docker Compose applications to Kubernetes, including inspection, planning, manifest generation, validation, deployment with approval, verification, and diagnosis.

An MCP server that enables AI agents to safely operate multi-cluster Kubernetes fleets through an Open Cluster Management hub, with policy enforcement, human approval, and audit tracing.