Lumino
@spre-sre
关于 Lumino
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.
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"lumino": {
"type": "stdio",
"command": "<ABSOLUTE_PATH_TO_LUMINO>/.venv/bin/python",
"args": [
"<ABSOLUTE_PATH_TO_LUMINO>/main.py"
],
"env": {
"PYTHONUNBUFFERED": "1"
}
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
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.
开发工具 分类下的更多 MCP 服务器
test
prysmaticlabsGo implementation of Ethereum proof of stake
Deepwiki MCP Server
regenrek📖 MCP server for fetch deepwiki.com and get latest knowledge in Cursor and other Code Editors
MCP-Scan: An MCP Security Scanner
invariantlabs-aiSecurity scanner for AI agents, MCP servers and agent skills.
Hello World MCP Server (Reference Extension)
anthropicsDesktop Extensions: One-click local MCP server installation in desktop apps
Unity MCP (Server + Plugin)
IvanMurzakAI Skills, MCP Tools, and CLI for Unity Engine. Full AI develop and test loop. Use cli for quick setup. Efficient token usage, advanced tools. Any C# method may be turned into a tool by a single line. Works with Claude Code, Gemini, Copilot, Cursor and any other absolutely for fr
评论