Lumino

by spre-sre

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GitHub

About

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.

Details

Author: spre-sre
Downloads: 307
Categories: Developer Tools, AI, Infrastructure

- 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)

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Lumino

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

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

JSON

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

JSON

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

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

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

JSON

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

JSON

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

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

From the repository

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

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
- Features
- Quick Start
- Prerequisites
- Installation
- Usage Examples
- Configuration
- Available Tools
- Architecture
- How It Works
- MCP Client Integration
- Performance Considerations
- Troubleshooting
- Dependencies
- Contributing
- Security
- License
- Acknowledgments

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

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)

```bash

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About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

LUMINO MCP Server

Table of Contents

Overview

Features

Kubernetes & OpenShift Operations

Tekton Pipeline Intelligence

Advanced Log Analysis

Predictive & Proactive Monitoring

Event Intelligence

Simulation & What-If Analysis

Quick Start

For Claude Code CLI Users (Easiest)

For Other MCP Clients

Verify Installation

Prerequisites

Required

For Kubernetes Features

Optional (Recommended)

Installation

Using uv (recommended)

Install

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