Lumino
@spre-sre
Lumino について
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.
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"lumino": {
"type": "stdio",
"command": "<ABSOLUTE_PATH_TO_LUMINO>/.venv/bin/python",
"args": [
"<ABSOLUTE_PATH_TO_LUMINO>/main.py"
],
"env": {
"PYTHONUNBUFFERED": "1"
}
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Lumino?
Lumino is an open source MCP (Model Context Protocol) server that empowers Site Reliability Engineers (SREs) and DevOps teams with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments. It exposes 37 specialized tools through MCP for monitoring, analysis, troubleshooting, prediction, and simulation.
How to use Lumino?
Clone the repository, install Python dependencies with uv sync (or pip install -e .), and run python main.py. For Claude Code CLI users, configure a .mcp.json file pointing to the installed server. Alternatively, use mcpm install @spre-sre/lumino-mcp-server. After installation, authenticate to a Kubernetes/OpenShift cluster and approve the MCP server to start using Lumino tools.
Key features of Lumino
- Kubernetes & OpenShift operations (namespace, pod, resource management)
- Tekton pipeline intelligence (monitoring, root cause analysis, baselining)
- Advanced log analysis (summarization, streaming, semantic search, anomaly detection)
- Predictive & proactive monitoring (statistical anomaly detection, resource forecasting)
- Event intelligence (smart retrieval, ML pattern detection, log-event correlation)
- Simulation & what-if analysis (Monte Carlo, impact analysis, risk assessment)
Use cases of Lumino
- Generate comprehensive RCA reports for failed pipeline runs
- Predict resource bottlenecks across namespaces for the next 48 hours
- Simulate configuration changes before deploying to production
- Map live topology of services, deployments, and dependencies
- Perform advanced forensic investigation with multi-faceted log/event analysis
- Establish CI/CD pipeline performance baselines and detect deviations
FAQ from Lumino
What are the prerequisites for using Lumino?
Python 3.10+, an MCP client (Claude Desktop, Claude Code CLI, Gemini CLI, or Cursor IDE), and a valid Kubernetes/OpenShift kubeconfig with read permissions.
How does authentication work?
Lumino automatically detects Kubernetes configuration: in-cluster config when running inside a pod, or local kubeconfig (~/.kube/config) when running locally.
What transports are supported?
Local mode uses stdio transport for direct MCP client integration. When the KUBERNETES_NAMESPACE environment variable is set, the server switches to HTTP streaming transport for Kubernetes‑native operation.
What dependencies are required?
Core: Python 3.10+, uv or pip. Optional but recommended: uv for faster dependency management, MCPM for easier installation, and Prometheus for advanced metrics and forecasting.
Does Lumino have any known limitations?
The README does not list any explicit known limitations.
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