AI-powered incident management MCP server that enables investigation, root cause analysis, and response actions for production incidents using mocked data for demo purposes.
Python-based MCP server for full-cycle incident management, enabling detection, root-cause analysis, and response guidance through tools like detect_incidents, analyze_incident, and suggest_response.
MCP server that provides guarded, audited, read-only access to ops tooling (alerts, metrics, logs, deploys, runbooks) and a triage agent that diagnoses incidents end-to-end with CI-verified root cause analysis.
Exposes an observability REST API as MCP tools, enabling incident investigation through automated correlation of deploys with errors, log and metrics queries.
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.