Lumino MCP Server

$git clone https://github.com/spre-sre/lumino-mcp-server.git && cd lumino-mcp-server && uv sync
README.md

AI/ML-powered diagnostic engine for SRE Observability on Konflux and OpenShift.

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 (6)

list_namespacesList all namespaces in the Kubernetes cluster.
analyze_logsSmart log summarization and anomaly detection using statistical and ML techniques.
troubleshoot_pipelineFailed pipeline root cause analysis for Tekton pipelines.
predict_bottlenecksResource bottleneck forecasting and predictive log analysis.
check_certificatesCertificate health monitoring with expiry alerts and TLS issue investigation.
simulate_config_changeMonte Carlo simulation for configuration changes to assess impact before deployment.

Environment Variables

PYTHONUNBUFFEREDrequiredEnsures python output is sent straight to terminal

Configuration

claude_desktop_config.json
{"mcpServers": {"lumino": {"type": "stdio", "command": "<ABSOLUTE_PATH_TO_LUMINO>/.venv/bin/python", "args": ["<ABSOLUTE_PATH_TO_LUMINO>/main.py"], "env": {"PYTHONUNBUFFERED": "1"}}}}

Try it

List all namespaces in my Kubernetes cluster
Analyze the logs for the failed Tekton pipeline in the production namespace and find the root cause.
Predict potential resource bottlenecks for my OpenShift cluster based on current metrics.
Check all TLS certificates in the cluster for upcoming expiry dates.
Simulate the impact of changing the memory limits on our core microservices.

Frequently Asked Questions

How do I install Lumino?

Install Lumino by running: git clone https://github.com/spre-sre/lumino-mcp-server.git && cd lumino-mcp-server && uv sync

What MCP clients work with Lumino?

Lumino works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Use Lumino with Conare

Manage MCP servers visually, upload persistent context, and never start from zero with Claude Code & Codex.

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