An open source MCP server empowering SREs with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments.
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.
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AI/ML-powered diagnostic engine for SRE Observability on Konflux and OpenShift. It uses the Model Context Protocol (MCP) and 40+ tools to analyze logs, metrics, and traces, enabling automated RCA and predictive analysis.
A comprehensive Model Context Protocol (MCP) server that exposes 216 tools, 7 resources, and 10 runbook prompts for every OpenShift 4 cluster operation an SRE, developer, or operator could need — all driven by an LLM.
An MCP server for autonomous Kubernetes troubleshooting and remediation. It enables continuous cluster monitoring, local AI-powered diagnosis via Ollama, and automated kubectl-based fixes.