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...
概览
An open source MCP (Model Context Protocol) server empowering SREs with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments. New to the SPRE MCP diagnostics stack (Lumino, PagerDuty, Slack, DevLake, ngit-memory, GitHub, GitLab, Konflux Portal, Sumo Logic, Jira)? Start here: - — setup, first diagnostic, troubleshooting, three worked examples, and expected tool outputs LUMINO MCP Server transforms how Site Reliability Engineers (SREs) and DevOps teams interact with Kubernetes clusters.
README
以下为原文 README 节选，完整内容请查看原文。
LUMINO MCP Server
An open source MCP (Model Context Protocol) server empowering SREs with intelligent observability, predictive analytics, and AI-driven automation across Kubernetes, OpenShift, and Tekton environments.
Table of Contents
- Overview
- Features
- Quick Start
- Team Onboarding Guide
- Prerequisites
- Installation
- Usage Examples
- Configuration
- ML Model Persistence
- KubeArchive Integration
- Prometheus/Thanos Integration
- Available Tools
- Architecture
- How It Works
- MCP Client Integration
- Performance Considerations
- Troubleshooting
- Dependencies
- Contributing
- Security
- License
- Acknowledgments
Team Onboarding Guide
New to the SPRE MCP diagnostics stack (Lumino, PagerDuty, Slack, DevLake, ngit-memory, GitHub, GitLab, Konflux Portal, Sumo Logic, Jira)? Start here:
- docs/ONBOARDING_GUIDE.md — setup, first diagnostic, troubleshooting, three worked examples, and expected tool outputs
Overview
LUMINO MCP Server transforms how Site Reliability Engineers (SREs) and DevOps teams interact with Kubernetes clusters. By exposing 39 specialized tools through the Model Context Protocol, it enables AI assistants to:
- Monitor cluster health, resources, and pipeline status in real-time
- Analyze logs, events, and anomalies using statistical and ML techniques
- Troubleshoot failed pipelines with automated root cause analysis
- Predict resource bottlenecks and potential issues before they occur
- Simulate configuration changes to assess impact before deployment
Features
Kubernetes & OpenShift Operations
- Namespace and pod management
- Resource querying with flexible output formats
- Label-based resource search across clusters
- OpenShift operator and MachineConfigPool status
- etcd log analysis
Tekton Pipeline Intelligence
- Pipeline and task run monitoring across namespaces
- Detailed log retrieval with optional cleaning
- Failed pipeline root cause analysis
- Cross-cluster pipeline tracing
- CI/CD performance baselining
Advanced Log Analysis
安装
This server does not publish a one-line install command.
Open the repository installation guide 新手？先看 mcpskill.org 教程 →配置
Provision the Lumino MCP server as a project-local MCP integration:
1. Clone the repository:
git clone https://github.com/spre-sre/lumino-mcp-server.git
2. Install Python dependencies using uv:
cd lumino-mcp-server && uv sync
3. Create .mcp.json in the current project root (NOT inside lumino-mcp-server) with this configuration.
IMPORTANT: Replace <ABSOLUTE_PATH_TO_LUMINO> with the actual absolute path to the cloned lumino-mcp-server directory:
{
"mcpServers": {
"lumino": {
"type": "stdio",
"command": "<ABSOLUTE_PATH_TO_LUMINO>/.venv/bin/python",
"args": ["<ABSOLUTE_PATH_TO_LUMINO>/main.py"],
"env": {
"PYTHONUNBUFFERED": "1"
}
}
}
}
4. After creating .mcp.json, inform the user to:
- Exit Claude Code completely
- Connect to their Kubernetes or OpenShift cluster (kubectl/oc login)
- Restart Claude Code in this project directory
- They will see a prompt to approve the Lumino MCP server
- Once approved, Lumino tools will be available (check with /mcp command)