"Kubernetes cluster management with Argo CD GitOps" matching MCP servers:

Gives an LLM agent a fixed, typed set of on-call tools over a self-hosted GitOps Kubernetes platform, reading metrics, logs, alerts and deploy history from Prometheus, Loki, Alertmanager and GitHub without any cluster write access. Rollbacks and config changes are proposed as one-shot ids that require explicit human confirmation before becoming pull requests against the Git repository, with both steps recorded in an audit log.

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 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.

An MCP server that gives AI assistants direct access to Kubernetes clusters for pod management and network debugging (DNS, NetworkPolicy, ingress, service mesh) and correlates Dynatrace observability with OpenTelemetry self-monitoring, which automatically ships traces, metrics, and logs to Dynatrace.

A read-only MCP server for Kubernetes that allows querying cluster information and diagnosing issues through natural language interfaces like Claude.

Enables log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

Enables an AI assistant to diagnose a broken application on a local Kubernetes cluster by inspecting pods, events, and logs, with server-side guardrails, least-privilege RBAC, and an audit log. It can also restart deployments in allowed namespaces only after a dry-run preview and host approval.

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.

Enables real-time Kubernetes cluster observability and debugging through standardized MCP interface. Provides access to pods, services, nodes, events, and includes built-in tools for cluster health analysis and issue identification.

Enables interactive Kubernetes cluster monitoring and troubleshooting through natural language queries. Users can diagnose pod issues, check service status, and investigate cluster problems using conversational AI.

Provides a set of read-only Kubernetes functions via an MCP server, enabling interaction with Kubernetes clusters through agents or coding assistants like GitHub Copilot.

Enables natural language Kubernetes cluster operations, SLO monitoring, and PromQL queries via Claude using the Model Context Protocol.

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.

Exposes Kubernetes cluster state with specialized telecom awareness of 5G Core network functions and topologies to MCP-compatible LLMs. It enables natural language analysis of 5G workloads, network slices, UPF data planes, and cluster health.

An MCP server that provides comprehensive monitoring and management capabilities for Teradata Workload Management (WLM), enabling tasks like performance troubleshooting, emergency throttling, and scheduled maintenance through 41 tools and 39 resources.

Enables plant operators to query alarm data, asset information, trends, correlations, and operator recommendations through 14 tools, forming a core part of a multi-MCP enterprise operations copilot.

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.

Enables AI agents to inspect and operate multiple Kubernetes clusters through natural language, providing multi-cluster diagnostics, RBAC analysis, security checks, and app-centric deployment with GitOps and smart workload placement.

Kubernetes MCP server built into Sextant, a free open-source desktop client: agents get per-cluster permissions (off by default), every change needs a confirmation in the window, and every call is logged. Tools cover resources, logs, events, metrics, scale, restart, Helm, Flux and Argo CD.

Manage Your Kubernetes Cluster with k8s mcp-server

Enables natural language analysis of network packet captures, including protocol detection, flow analysis, and security threat identification, integrated with AI assistants.

Enables AI assistants to perform DevOps tasks including Kubernetes management, cloud provider operations, CI/CD, security scanning, and infrastructure monitoring through natural language.

Enables Claude Code agents to judge AppStore staging rollouts by reading Argo CD, Kubernetes pod/rollout/log/event, CloudNativePG, health, certificate, node, and GitHub Actions/PR data through read-only, redacted tools.

Exposes Kubernetes cluster management tools to LLMs, enabling querying pods, deployments, logs, metrics, and managing port forwards via natural language.

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

Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.

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