Lumino MCP Server

@spre-sre

代码仓库

基本信息

分类

开发工具

传输方式

stdio

作者

spre-sre

提交者

geored

关于 Lumino MCP Server

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.

#mcp #model-context-protocol #kubernetes #openshift#tekton#sre #observability #ai #machine-learning #devops#cicd#prometheus #log-analysis#root-cause-analysis#konflux#claude #anthropic #mcp-server

Lumino MCP Server 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"
      }
    }
  }
}

Lumino MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

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

常见问题

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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