"How to execute EKS (Elastic Kubernetes Service) commands" matching MCP servers:

A Model Context Protocol server that provides programmatic access to Amazon EKS clusters, allowing AI assistants to manage and interact with Kubernetes resources. It exposes cluster operations like listing pods as callable tools through a standardized interface.

Enables MCP clients to inspect and operate Kubernetes clusters across multiple contexts, with read-only/read-write/admin access modes and security flags controlling namespaces, contexts, deletes, applies, and exec.

Enables AI assistants to interact with Kubernetes clusters through 50 specialized tools for comprehensive cluster management. Supports both local kubectl and remote SSH-based execution for managing pods, deployments, services, and other Kubernetes resources.

Enterprise-grade MCP server for Kubernetes operations, supporting local kubeconfig and AWS EKS authentication, with read, write, and destructive operations protected by multi-layer safety guards.

A Model Context Protocol (MCP) server that provides safe, read-only access to Kubernetes resources for debugging and inspection. Built with security in mind, it offers comprehensive cluster visibility without modification capabilities.

Enables inspecting and managing Kubernetes clusters through MCP, including listing deployments and pods, retrieving logs and events, and scaling or restarting deployments.

Enables secure Kubernetes operations with configurable security modes, supporting core Kubernetes resources, Istio service mesh, and Helm chart management through natural language.

Enables AI assistants to manage Docker containers and Kubernetes resources through natural language, supporting operations like container management, image building, and pod/deployment/service management.

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.

A lightweight MCP server that enables natural language interaction with Kubernetes clusters, allowing management of pods, deployments, namespaces, and cluster resources through conversational queries or API endpoints.

A powerful and flexible Kubernetes MCP server implementation with support for OpenShift.

Enables comprehensive Kubernetes cluster management through kubectl operations and Helm chart management. Supports resource operations, logging, scaling, rollouts, and diagnostics with multiple transport modes and security features.

Enables advanced management of Kubernetes clusters through natural language interactions. Supports querying, managing, and monitoring pods, deployments, nodes, and logs across multiple contexts and namespaces.

An MCP server that enables interaction with Kubernetes resources through natural language interfaces like Goose CLI, allowing users to get, read, and patch Kubernetes resources.

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

Enables comprehensive Kubernetes cluster management through kubectl operations, Helm chart deployments, pod troubleshooting, and node management. Supports both read-only and full cluster administration capabilities with built-in safety features.

A Model Control Protocol server that extends AI assistants with Kubernetes operations capabilities, allowing for managing deployments, pods, services and other K8s resources.

Provides secure access to containerized build environments for software projects, enabling AI assistants to execute builds, run tests, manage git operations, and inspect build artifacts without requiring local installation of dependencies.

Enables comprehensive benchmarking and performance monitoring of OpenShift clusters using OVN-Kubernetes networking through automated data collection, AI-powered analysis, and report generation. Provides intelligent insights into cluster performance, bottleneck detection, and optimization recommendations.