A Model Context Protocol server that enables monitoring and analysis of Precision Time Protocol systems in OpenShift clusters through configuration parsing, log monitoring, and natural language queries.
Enables diagnostics and troubleshooting of OpenShift clusters through storage analysis, resource monitoring, GPU utilization tracking, and pod health checks using Prometheus metrics and the oc CLI.
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.
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.
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.
Provides AI assistants with direct access to Red Hat OpenShift AI observability data, enabling querying of Prometheus metrics, Alertmanager alerts, Loki logs, Grafana dashboards, and Kubernetes cluster state to troubleshoot vLLM inference workloads.
