"Setting up an MCP server in Kubernetes" matching MCP servers:

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

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 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 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.

Enables AI agents to manage a Canonical Landscape estate, including inventory, alerts, patching, and script execution, with built-in safety layers to prevent accidental destructive actions.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Self-hosted Nightingale (n9e) includes a native Streamable HTTP MCP server at /mcp for querying alerts, rules, metrics, logs, hosts and dashboards with the caller's existing permissions. Users deploy the Nightingale server themselves; the implementation and deployment documentation are in the main repository, and the official website is https://n9e.io.

Enables state-aware Terraform/OpenTofu plan evaluation against live AWS topology and CloudWatch telemetry, detecting reliability blast radius and FinOps waste before merge.

MCP server that wraps Nightjar's analytics API, enabling Claude Code to query events, funnels, dashboards, and metrics through 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.

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.

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.

Enables AI assistants to interact with and manage Kubernetes clusters, supporting operations on pods, deployments, services, configmaps, secrets, namespaces, metrics, and events with built-in safety features for destructive actions.

An MCP server for managing and monitoring Docker, Docker Compose, and Kubernetes environments alongside Azure Application Insights. It enables advanced log filtering, container lifecycle management, and querying of cloud application traces and metrics.

Maintenant modern monitoring for docker and kubernetes. NOTE : Is local server, you can't see online deployments

An MCP server that enables Claude to manage infrastructure across Kubernetes, Docker, Prometheus, and Terraform through natural language. It provides over 42 specialized tools with a safety-first design, including risk-based command classification and audit logging.

Enables read-only, stack-aware diagnostics for microservices on Kubernetes, turning debugging runbooks into conversational tools that investigate slow requests, changes, leaks, and in-pod conditions without exposing cluster credentials.

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.

Lets an AI agent add websites, set up and tune uptime, user-flow, security, speed, SEO, domain and compliance monitors, run scans, and review results. It also explains what broke in a failed run and proposes fixes the agent can apply with the user.

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.

Enables retrieval, setting, and detailed information lookup of EPICS process variables (PVs) for monitoring and controlling hardware or software parameters.