README.md
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.
Overview
LUMINO MCP Server transforms how Site Reliability Engineers (SREs) and DevOps teams interact with Kubernetes clusters. By exposing 37 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:
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)
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:
"List all namespaces in my Kubernetes cluster"
Prerequisites
Tools 5
kubernetes_operationsManage namespaces, pods, and query cluster resources with label-based search.tekton_pipeline_intelligenceMonitor pipeline status, retrieve logs, and perform root cause analysis on failed runs.log_analysisPerform smart log summarization, anomaly detection, and semantic search.predictive_monitoringForecast resource bottlenecks and monitor certificate health.simulation_analysisRun Monte Carlo simulations for configuration changes and impact assessment.Environment Variables
PYTHONUNBUFFEREDEnsures Python output is sent directly to the terminal without buffering.Try it
→List all namespaces in my Kubernetes cluster and identify any pods that are not in a running state.
→Analyze the logs for the failed Tekton pipeline run and provide a root cause analysis.
→Predict potential resource bottlenecks for the production namespace over the next 24 hours.
→Run a Monte Carlo simulation to assess the impact of increasing memory limits on the deployment.
→Check the status of all TLS certificates in the cluster and alert on any nearing expiry.