"Kubernetes" matching MCP servers:

Provides read-only access to Kubernetes clusters for AI assistants.

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.

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 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.

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

A satirical MCP server that diagnoses Kubernetes incidents through theological doctrine and provides real kubectl remediation steps.

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 comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

MCP Server for Kubernetes Audit Logs.

Enables AI agents to inspect Kubernetes pods and cluster events, query Prometheus and Loki, and diagnose pod health with suggested actions through MCP tools.

Enables read-only Kubernetes cluster diagnostics through MCP tools, allowing a local LLM to inspect pods and events and debug issues via natural language queries.

Enables AI agents to safely observe and troubleshoot Kubernetes workloads, with limited recovery actions like pod deletion and Helm rollbacks, while preventing dangerous modifications.

An MCP server that lets AI assistants safely inspect and operate on Kubernetes clusters through natural conversation.

MCP server for Kubernetes FinOps that finds over-provisioned workloads, proposes right-sizing, and enforces safety guardrails with dry-run by default and audit logging.

Enables interaction with Prometheus Alertmanager for querying alerts, managing silences, and investigating incidents. Supports direct API connection and Kubernetes auto-discovery.

Retrieves logs from Datadog for a specified Kubernetes cluster and namespace, enabling analysis by Claude through natural language queries.