Lumino

Name: Lumino
Author: spre-sre

@spre-sre

About Lumino

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

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

FAQ from Lumino

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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