"kubernetes" matching MCP servers:

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

Enables AI agents to interact with Kubernetes clusters through progressive disclosure, where agents discover TypeScript modules via filesystem, write and execute code, and receive summarized console output for cluster management tasks.

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.

Delegates expensive or long-running AI tasks to Kubernetes-hosted Claude agents running on OpenShift. Enables creating, managing, and monitoring remote agentic sessions for complex work like codebase analysis and security audits.

A gateway for Generative AI systems to interact with multiple Kubernetes clusters through Model Context Protocol, enabling comprehensive Kubernetes resource operations and multi-cluster management.

Enables AI-powered management of multi-cluster Kubernetes environments through natural language, supporting kubectl operations, function execution, and agent interactions with multiple AI providers.

Enables AI agents to plan, submit, monitor, and manage Kubeflow training jobs through natural language, without needing to learn Kubernetes or the Kubeflow SDK.

Enables running durable, traceable AI agents via LangGraph through a universal MCP interface, integrating with Hatchet for orchestration, logging, and retries. Provides tools for knowledge management (ingestion, RAG) and Kubernetes operations (diagnosis, auto-fix).

An MCP server that enables interaction with Kubernetes/Minikube clusters through natural language, allowing AI agents like Codename Goose to manage Kubernetes resources via the Model Context Protocol.

Enables intelligent Kubernetes deployments, automated documentation testing, organizational pattern management, and shared prompt libraries. Provides AI-powered recommendations based on cluster capabilities and automates DevOps workflows through conversational interfaces.

Enables Kubernetes-native management of agent/model workloads via MCP tools, including fleet status, workload lifecycle, and boot orchestration for AI workflows.

Enables AI agents to dynamically spawn, route to, and manage ephemeral containerized sandboxes on Kubernetes without requiring Custom Resource Definitions or cluster-level operators.

Provides MCP multi-cluster Kubernetes management and operations, featuring a management interface, logging, and nearly 50 built-in tools covering common DevOps and development scenarios. Supports both standard and CRD resources.

Manages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.

A decentralized, secure agent orchestration framework for monitoring and managing distributed edge servers, Docker hosts, and Kubernetes nodes via outbound SSH tunnels with zero inbound ports.

Autonomous AI agent for Kubernetes triage and infrastructure management using natural language.

A hardened MCP gateway that lets AI agents securely call infrastructure APIs like Kubernetes, Terraform, and Jenkins, with authentication, rate limiting, and OPA policy enforcement.

Enables delegation of agentic sessions to Kubernetes-hosted Claude agents running on the Ambient Code Platform. Supports creating, managing, and communicating with remote AI agent sessions through OpenShift authentication.