spre-sre/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
- Smart log summarization with configurable detail levels
- Streaming analysis for large log volumes
- Hybrid analysis combining multiple strategies
- Semantic search using NLP techniques
- Anomaly detection with severity classification
Predictive & Proactive Monitoring
- Statistical anomaly detection using z-score analysis
- Predictive log analysis for early warning
- Resource bottleneck forecasting
- Certificate health monitoring with expiry alerts
- TLS certificate issue investigation
Event Intelligence
- Smart event retrieval with multiple strategies
- Progressive event analysis (overview to deep-dive)
- Advanced analytics with ML pattern detection
- Log-event correlation
Simulation & What-If Analysis
- Monte Carlo simulation for configuration changes
- Impact analysis before deployment
- Risk assessment with configurable tolerance
- Affected component identification
Quick Start
Get started with LUMINO in under 2 minutes:
For Claude Code CLI Users (Easiest)
Simply ask Claude Code to provision the Lumino MCP server for you by pasting this prompt:
For Other MCP Clients
Choose your preferred installation method:
- **MCPM (Recommended)**: mcpm install @spre-sre/lumino-mcp-server
- **Manual Setup**: See detailed MCP Client Integration instructions
Verify Installation
Once installed, test with a simple query:
Prerequisites
Required
- **Python 3.10 or higher** - Core runtime
- **MCP Client** - One of:
- Claude Desktop
- Claude Code CLI
- Gemini CLI
- Cursor IDE
For Kubernetes Features
- **Kubernetes/OpenShift Access** - Valid kubeconfig with read permissions
- **RBAC Permissions** - Ability to list pods, namespaces, and other resources
Optional (Recommended)
- **uv** - Faster dependency management than pip
- **MCPM** - Easiest installation experience
- **Prometheus** - For advanced metrics and forecasting features
Installation
Using uv (recommended)
Using pip
Usage
Local Mode (stdio transport)
By default, the server runs in local mode using stdio transport, suitable for direct integration with MCP clients:
Kubernetes Mode (HTTP streaming transport)
When running inside Kubernetes, set the namespace environment variable to enable HTTP streaming:
The server automatically detects the environment and switches transport modes.
Usage Examples
🔍 Intelligent Root Cause Analysis
Investigate and diagnose complex failures with automated analysis:
🎯 Predictive Intelligence & Forecasting
Anticipate problems before they impact your systems:
🧪 Simulation & What-If Analysis
Test changes safely before applying them to production: