Lumino MCP Server

Official

by spre-sre · Python

AI-powered SRE observability for Kubernetes/OpenShift with 40+ Tekton debugging tools

AI-powered observability for Kubernetes/OpenShift with 40+ debugging tools.

README.md34 KBView on GitHub

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:

code

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:

code

"List all namespaces in my Kubernetes cluster"

Prerequisites

Required

Python 3.10 or higher - Core runtime
MCP Client - One of:

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

# Clone the repository
git clone https://github.com/spre-sre/lumino-mcp-server.git
cd lumino-mcp-server

# Install dependencies
uv sync

# Run the server
uv run python main.py

Using pip

bash

# Clone the repository
git clone https://github.com/spre-sre/lumino-mcp-server.git
cd lumino-mcp-server

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

# Run the server
python main.py

Usage

Local Mode (stdio transport)

By default, the server runs in local mode using stdio transport, suitable for direct integration with MCP clients:

bash

python main.py

Kubernetes Mode (HTTP streaming transport)

When running inside Kubernetes, set the namespace environment variable to enable HTTP streaming:

bash

export KUBERNETES_NAMESPACE=my-namespace
python main.py

The server automatically detects the environment and switches transport modes.

Usage Examples

🔍 Intelligent Root Cause Analysis

Investigate and diagnose complex failures with automated analysis:

code

"Generate a comprehensive RCA report for the failed pipeline run 'build-api-pr-456' in namespace ci-cd"

code

"Analyze what caused pod crashes in namespace production over the last 6 hours and correlate with resource events"

code

"Investigate the TLS certificate issues affecting services in namespace ingress-nginx"

🎯 Predictive Intelligence & Forecasting

Anticipate problems before they impact your systems:

code

"Predict resource bottlenecks across all production namespaces for the next 48 hours"

code

"Analyze historical pipeline performance and detect anomalies in build times for the last 30 days"

code

"Check cluster certificate health and alert me about any certificates expiring in the next 60 days"

code

"Use predictive log analysis to identify potential failures in namespace monitoring before they occur"

🧪 Simulation & What-If Analysis

Test changes safely before applying them to production:

code

"Simulate the impact of increasing memory limits to 4Gi for all pods in namespace backend-services"

code

"Run a what-if scenario for scaling deployments to 10 replicas and analyze resource consumption"

code

"Simulate configuration changes for nginx ingress controller and assess risk to existing traffic"

🗺️ Topology & Dependency Mapping

Understand system architecture and component relationships:

code

"Generate a live topology map of all services, deployments, and their dependencies in namespace microservices"

code

"Map the complete dependency graph for the payment-service including all connected resources"

code

"Show me the topology of components affected by the cert-manager service"

🔬 Advanced Investigation & Forensics

Deep-dive into complex issues with multi-faceted analysis:

code

"Perform an adaptive namespace investigation for production - analyze logs, events, and resource patterns"

code

"Create a detailed investigation report for resource constraints and bottlenecks in namespace data-processing"

code

"Trace pipeline execution for commit SHA abc123def from source to deployment across all namespaces"

code

"Search logs semantically for 'authentication failures related to expired tokens' across the last 24 hours"

📊 CI/CD Pipeline Intelligence

Optimize and troubleshoot your continuous delivery pipelines:

code

"Establish performance baselines for all Tekton pipelines and flag runs deviating by more than 2 standard deviations"

code

"Trace the complete pipeline flow for image 'api:v2.5.3' from build to production deployment"

code

"Analyze failed pipeline runs in namespace tekton-pipelines and identify common failure patterns"

code

"Compare current pipeline run times against 30-day baseline and highlight performance degradation"

🎨 Progressive Event Analysis

Multi-level event investigation from overview to deep-dive:

code

"Start with an overview of events in namespace kube-system, then drill down into critical issues"

code

"Perform advanced event analytics with ML pattern detection for namespace monitoring over the last 12 hours"

code

"Correlate events with pod logs to identify the root cause of CrashLoopBackOff in namespace applications"

🚀 Real-Time Monitoring & Alerts

Stay informed about cluster health and pipeline status:

code

"Show me the status of all Tekton pipeline runs cluster-wide and highlight long-running pipelines"

code

"List all failed TaskRuns in the last hour with error details and recommended actions"

code

"Monitor OpenShift cluster operators and alert on any degraded components"

code

"Check MachineConfigPool status and show which nodes are being updated"

🔐 Security & Compliance

Ensure cluster security and certificate management:

code

"Scan all namespaces for expiring certificates and generate a renewal schedule"

code

"Investigate TLS certificate issues causing handshake failures in namespace istio-system"

code

"Audit all secrets and configmaps for sensitive data exposure patterns"

📈 Advanced Analytics & ML Insights

Leverage machine learning for pattern detection:

code

"Use streaming log analysis to process large log volumes from namespace data-pipeline with error pattern detection"

code

"Detect anomalies in log patterns using ML analysis with medium severity threshold for namespace api-gateway"

code

"Analyze resource utilization trends using Prometheus metrics and forecast capacity needs"

Configuration

Kubernetes Authentication

The server automatically detects Kubernetes configuration:

In-cluster config - When running inside a Kubernetes pod
Local kubeconfig - When running locally (uses ~/.kube/config)

Environment Variables

Variable	Description	Default	When to Use
`KUBERNETES_NAMESPACE`	Namespace for K8s mode	-	When running server inside a Kubernetes pod
`K8S_NAMESPACE`	Alternative namespace variable	-	Alternative to `KUBERNETES_NAMESPACE`
`PROMETHEUS_URL`	Prometheus server URL for metrics	Auto-detected	Custom Prometheus endpoint or non-standard port
`KUBECONFIG`	Path to kubeconfig file	`~/.kube/config`	Multiple clusters or custom kubeconfig location
`LOG_LEVEL`	Logging verbosity (DEBUG, INFO, WARNING, ERROR)	`INFO`	Debugging issues or reducing log noise
`MCP_SERVER_LOG_LEVEL`	MCP framework log level	`INFO`	Troubleshooting MCP protocol issues
`PYTHONUNBUFFERED`	Disable Python output buffering	-	Recommended for MCP clients to see real-time logs

Available Tools

Kubernetes Core (5 tools)

Tool	Description
`list_namespaces`	List all namespaces in the cluster
`list_pods_in_namespace`	List pods with status and placement info
`get_kubernetes_resource`	Get any Kubernetes resource with flexible output
`search_resources_by_labels`	Search resources across namespaces by labels
`query_kubearchive`	Query archived Kubernetes resources from KubeArchive with optional log retrieval

Tekton Pipelines (6 tools)

Tool	Description
`list_pipelineruns`	List PipelineRuns with status and timing
`list_taskruns`	List TaskRuns, optionally filtered by pipeline
`get_pipelinerun_logs`	Retrieve pipeline logs with optional cleaning
`list_recent_pipeline_runs`	Recent pipelines across all namespaces
`find_pipeline`	Find pipelines by pattern matching
`get_tekton_pipeline_runs_status`	Cluster-wide pipeline status summary

Log Analysis (6 tools)

Tool	Description
`analyze_logs`	Extract error patterns from log text
`smart_summarize_pod_logs`	Intelligent log summarization
`stream_analyze_pod_logs`	Streaming analysis for large logs
`analyze_pod_logs_hybrid`	Combined analysis strategies
`detect_log_anomalies`	Anomaly detection with severity levels
`semantic_log_search`	NLP-based semantic log search

Event Analysis (3 tools)

Tool	Description
`smart_get_namespace_events`	Smart event retrieval with strategies
`progressive_event_analysis`	Multi-level event analysis
`advanced_event_analytics`	ML-powered event pattern detection

Failure Analysis & RCA (2 tools)

Tool	Description
`analyze_failed_pipeline`	Root cause analysis for failed pipelines
`automated_triage_rca_report_generator`	Automated incident reports

Resource Monitoring (4 tools)

Tool	Description
`check_resource_constraints`	Detect resource issues in namespace
`detect_anomalies`	Statistical anomaly detection
`prometheus_query`	Execute PromQL queries
`resource_bottleneck_forecaster`	Predict resource exhaustion

Namespace Investigation (2 tools)

Tool	Description
`conservative_namespace_overview`	Focused namespace health check
`adaptive_namespace_investigation`	Dynamic investigation based on query

Certificate & Security (2 tools)

Tool	Description
`investigate_tls_certificate_issues`	Find TLS-related problems
`check_cluster_certificate_health`	Certificate expiry monitoring

OpenShift Specific (3 tools)

Tool	Description
`get_machine_config_pool_status`	MachineConfigPool status and updates
`get_openshift_cluster_operator_status`	Cluster operator health
`get_etcd_logs`	etcd log retrieval and analysis

CI/CD Performance (2 tools)

Tool	Description
`ci_cd_performance_baselining_tool`	Pipeline performance baselines
`pipeline_tracer`	Trace pipelines by commit, PR, or image

Topology & Prediction (2 tools)

Tool	Description
`live_system_topology_mapper`	Real-time system topology mapping
`predictive_log_analyzer`	Predict issues from log patterns

Simulation (1 tool)

Tool	Description
`what_if_scenario_simulator`	Simulate configuration changes

Architecture

code

lumino-mcp-server/
├── main.py                 # Entry point with transport detection
├── src/
│   ├── server-mcp.py       # MCP server with all 37 tools
│   └── helpers/
│       ├── constants.py    # Shared constants
│       ├── event_analysis.py    # Event processing logic
│       ├── failure_analysis.py  # RCA algorithms
│       ├── log_analysis.py      # Log processing
│       ├── resource_topology.py # Topology mapping
│       ├── semantic_search.py   # NLP search
│       └── utils.py             # Utility functions
└── pyproject.toml          # Project configuration

How It Works

LUMINO acts as a bridge between AI assistants and your Kubernetes infrastructure through the Model Context Protocol:

code

┌─────────────────────────────────────────────────────────────────┐
│                        AI Assistant Layer                        │
│          (Claude Desktop, Claude Code CLI, Gemini CLI)          │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             │ Natural Language Queries
                             │ "Analyze failed pipelines"
                             │ "Predict resource bottlenecks"
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                     Model Context Protocol                       │
│                      (MCP Communication)                         │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             │ Tool Invocations & Results
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                       LUMINO MCP Server                          │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐         │
│  │ Log Analysis │  │ Event Intel  │  │  Predictive  │         │
│  │   (6 tools)  │  │  (3 tools)   │  │  (2 tools)   │         │
│  └──────────────┘  └──────────────┘  └──────────────┘         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐         │
│  │   Pipeline   │  │  Simulation  │  │   Topology   │         │
│  │  (6 tools)   │  │  (1 tool)    │  │  (2 tools)   │         │
│  └──────────────┘  └──────────────┘  └──────────────┘         │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             │ Kubernetes API Calls
                             │ Prometheus Queries
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                    Kubernetes/OpenShift Cluster                  │
│                                                                   │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐       │
│  │   Pods   │  │ Services │  │ Tekton   │  │etcd/Logs │       │
│  └──────────┘  └──────────┘  │Pipelines │  └──────────┘       │
│  ┌──────────┐  ┌──────────┐  └──────────┘  ┌──────────┐       │
│  │  Events  │  │ Configs  │  ┌──────────┐  │Prometheus│       │
│  └──────────┘  └──────────┘  │OpenShift │  └──────────┘       │
│                               │Operators │                       │
│                               └──────────┘                       │
└─────────────────────────────────────────────────────────────────┘

Workflow

User Query → AI assistant receives natural language request
MCP Translation → Assistant converts query to appropriate tool calls
LUMINO Processing → Server executes Kubernetes/Prometheus operations
Data Analysis → ML/statistical algorithms process raw data
AI Synthesis → Assistant formats results into human-readable insights

Key Features

Stateless Design - No data persistence, queries cluster in real-time
Automatic Transport Detection - Switches between stdio (local) and HTTP (K8s) modes
Token Budget Management - Adaptive strategies to handle large log volumes
Intelligent Caching - Smart caching for frequently accessed data
Security First - Uses existing kubeconfig RBAC permissions, no separate auth

MCP Client Integration

Method 1: Using MCPM (Recommended for Claude Code CLI / Gemini CLI)

The easiest way to install LUMINO MCP Server for Claude Code CLI or Gemini CLI is using MCPM - an MCP server package manager.

Install MCPM

bash

# Clone and build MCPM
git clone https://github.com/spre-sre/mcpm.git
cd mcpm
go build -o mcpm .

# Optional: Add to PATH
sudo mv mcpm /usr/local/bin/

Requirements: Go 1.23+, Git, Python 3.10+, uv (or pip)

Install LUMINO MCP Server

bash

# Install from GitHub repository (short syntax)
mcpm install @spre-sre/lumino-mcp-server

# Or use full GitHub URL
mcpm install https://github.com/spre-sre/lumino-mcp-server.git

# For GitLab repositories (if hosted on GitLab)
mcpm install gl:@spre-sre/lumino-mcp-server

# Install for specific client
mcpm install @spre-sre/lumino-mcp-server --claude  # For Claude Code CLI
mcpm install @spre-sre/lumino-mcp-server --gemini  # For Gemini CLI

# Install globally (works with both Claude and Gemini)
mcpm install @spre-sre/lumino-mcp-server --global

Short syntax explained:

@owner/repo - Installs from GitHub (default: https://github.com/owner/repo.git)
gl:@owner/repo - Installs from GitLab (https://gitlab.com/owner/repo.git)
Full URL - Works with any Git repository

This will:

Clone the repository to ~/.mcp/servers/lumino-mcp-server/
Auto-detect Python project and install dependencies using uv (or pip)
Register with Claude Code CLI or Gemini CLI configuration automatically

Manage LUMINO

bash

# List installed servers
mcpm list

# Update LUMINO
mcpm update lumino-mcp-server

# Remove LUMINO
mcpm remove lumino-mcp-server

Method 2: Manual Configuration

If you prefer manual setup or need to configure Claude Desktop / Cursor, follow these client-specific guides:

Claude Desktop

Find your config file location:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- Linux: ~/.config/Claude/claude_desktop_config.json
Add LUMINO configuration:

json

{
  "mcpServers": {
    "lumino": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/lumino-mcp-server",
        "python",
        "main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Restart Claude Desktop
Verify: Look for the hammer icon (🔨) in Claude Desktop to see available tools

Claude Code CLI

Option A: Using MCPM (see Method 1 above)

Option B: Automatic Provisioning via Claude Code (Recommended and easiest way)

Copy and paste the provisioning prompt from the Quick Start section above into Claude Code. Claude will clone the repository, install dependencies, and configure the MCP server for your project.

Option C: Manual Configuration

Clone and install:

bash

git clone https://github.com/spre-sre/lumino-mcp-server.git
cd lumino-mcp-server
uv sync  # Creates .venv with all dependencies

Create .mcp.json in your project root (for project-local config) or update ~/.claude.json (for global config):

json

{
  "mcpServers": {
    "lumino": {
      "type": "stdio",
      "command": "/absolute/path/to/lumino-mcp-server/.venv/bin/python",
      "args": ["/absolute/path/to/lumino-mcp-server/main.py"],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Important: Replace /absolute/path/to/lumino-mcp-server with the actual absolute path where you cloned the repository (e.g., /Users/username/projects/lumino-mcp-server).

Verify installation:

bash

# Check MCP servers
claude mcp list

# Test with a query
claude "List all namespaces in my cluster"

Gemini CLI

Option A: Using MCPM (Recommended - see Method 1 above)

Option B: Manual Configuration

Find your config file location:
- macOS/Linux: ~/.config/gemini/mcp_servers.json
- Windows: %APPDATA%\gemini\mcp_servers.json
Add LUMINO configuration:

json

{
  "mcpServers": {
    "lumino": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/lumino-mcp-server",
        "python",
        "main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Verify installation:

bash

# Check MCP servers
gemini mcp list

# Test with a query
gemini "Show me failed pipeline runs"

Cursor IDE

Open Cursor Settings:
- Press Cmd+, (macOS) or Ctrl+, (Windows/Linux)
- Search for "MCP" or "Model Context Protocol"
Add MCP Server Configuration:

In Cursor's MCP settings, add:

json

{
  "mcpServers": {
    "lumino": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/lumino-mcp-server",
        "python",
        "main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Alternative - Using Cursor's settings.json:

Open Command Palette (Cmd+Shift+P or Ctrl+Shift+P)
Type "Preferences: Open User Settings (JSON)"
Add the MCP configuration:

json

{
  "mcp.servers": {
    "lumino": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/lumino-mcp-server",
        "python",
        "main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

Restart Cursor IDE
Verify: Open Cursor's AI chat and check if LUMINO tools are available

Configuration Notes

Replace /path/to/lumino-mcp-server with the actual path where you cloned the repository:

bash

# Example paths:
# macOS/Linux: /Users/username/projects/lumino-mcp-server
# Windows: C:\Users\username\projects\lumino-mcp-server

# If installed via MCPM:
# ~/.mcp/servers/lumino-mcp-server/

Environment Variables (optional):

Add these to the env section if needed:

json

{
  "env": {
    "PYTHONUNBUFFERED": "1",
    "KUBERNETES_NAMESPACE": "default",
    "PROMETHEUS_URL": "http://prometheus:9090",
    "LOG_LEVEL": "INFO"
  }
}

Using Alternative Python Package Managers

With pip instead of uv

json

{
  "command": "python",
  "args": [
    "/path/to/lumino-mcp-server/main.py"
  ]
}

Note: Ensure you've activated the virtual environment first:

bash

cd /path/to/lumino-mcp-server
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .

With poetry

json

{
  "command": "poetry",
  "args": [
    "run",
    "python",
    "main.py"
  ],
  "cwd": "/path/to/lumino-mcp-server"
}

Testing Your Configuration

After configuring any client, test the connection:

Check if tools are loaded:
- Claude Desktop: Look for 🔨 hammer icon
- Claude Code CLI: claude mcp list
- Gemini CLI: gemini mcp list
- Cursor: Check AI chat for available tools
Test a simple query:

code

"List all namespaces in my Kubernetes cluster"

Check server logs (if issues):

bash

# Run server manually to see errors
cd /path/to/lumino-mcp-server
uv run python main.py

Expected output:

code

MCP Server running in stdio mode
Available tools: 37
Waiting for requests...

Advanced Configuration

Multiple Clusters

Configure multiple LUMINO instances for different clusters:

json

{
  "mcpServers": {
    "lumino-prod": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/lumino-mcp-server", "python", "main.py"],
      "env": {
        "KUBECONFIG": "/path/to/prod-kubeconfig.yaml"
      }
    },
    "lumino-dev": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/lumino-mcp-server", "python", "main.py"],
      "env": {
        "KUBECONFIG": "/path/to/dev-kubeconfig.yaml"
      }
    }
  }
}

Custom Log Level

json

{
  "env": {
    "LOG_LEVEL": "DEBUG",
    "MCP_SERVER_LOG_LEVEL": "DEBUG"
  }
}

Supported Transports

The server automatically detects the appropriate transport:

stdio - For local desktop integrations (Claude Desktop, Claude Code CLI, Gemini CLI, Cursor)
streamable-http - For Kubernetes deployments (when KUBERNETES_NAMESPACE is set)

Performance Considerations

Optimizing for Large Clusters

LUMINO is designed to handle clusters of any size efficiently:

Cluster Size	Recommendation	Tool Strategy
Small (< 50 pods)	Use default settings	All tools work optimally
Medium (50-500 pods)	Use namespace filtering	Leverage adaptive tools with auto-sampling
Large (500+ pods)	Specify time windows and namespaces	Use conservative and streaming tools
Very Large (1000+ pods)	Combine filters and pagination	Progressive analysis with targeted queries

Token Budget Management

LUMINO automatically manages AI context limits:

Adaptive Sampling - Smart tools auto-sample data when volumes are high
Progressive Loading - Stream analysis processes data in chunks
Token Budgets - Configurable limits prevent context overflow
Hybrid Strategies - Automatically selects best analysis approach

Query Optimization Tips

Use Namespace Filtering

code

✅ "Analyze logs for pods in namespace production"
❌ "Analyze all pod logs in the cluster"

Specify Time Windows

code

✅ "Show events from the last 2 hours"
❌ "Show all events" (might return thousands)

Leverage Smart Tools

code

✅ "smart_summarize_pod_logs" - Adaptive analysis
❌ Direct log dumps - No processing

Use Progressive Analysis

code

✅ Start with "overview" → drill down to "detailed"
❌ Jump directly to "deep_dive" on large datasets

Performance Metrics

Operation	Typical Response Time	Scalability
List namespaces	< 1s	O(1)
Get pod logs (1 pod)	1-3s	O(log size)
Analyze pipeline run	2-5s	O(task count)
Cluster-wide search	5-15s	O(namespace count)
ML anomaly detection	3-10s	O(data points)
Topology mapping	5-20s	O(resource count)

Caching Strategy

LUMINO uses intelligent caching for frequently accessed data:

15-minute cache - For web-fetched content
Session cache - For hybrid log analysis
No persistence - All data queries cluster in real-time

Concurrent Requests

The server handles multiple concurrent requests efficiently:

Thread-safe operations - Safe parallel tool execution
Connection pooling - Reuses Kubernetes API connections
Async HTTP - Non-blocking Prometheus queries

Resource Usage

Server Resource Requirements

Deployment	CPU	Memory	Disk
Local (stdio)	100-500m	256-512Mi	Minimal
Kubernetes	200m-1	512Mi-1Gi	Minimal
High-load	1-2	1-2Gi	Minimal

Note: LUMINO is stateless and requires minimal resources. Most processing happens in the AI assistant.

Troubleshooting

Common Issues

No Kubernetes cluster found

code

Error: Unable to load kubeconfig

Ensure you have a valid kubeconfig at ~/.kube/config or are running inside a cluster.

Permission denied for resources

code

Error: Forbidden - User cannot list resource

Check your RBAC permissions. The server needs read access to the resources you want to query.

Tool timeout For large clusters, some tools may timeout. Use filtering options (namespace, labels) to reduce scope.

Dependencies

mcp[cli]>=1.10.1 - Model Context Protocol SDK
kubernetes>=32.0.1 - Kubernetes Python client
pandas>=2.0.0 - Data analysis
scikit-learn>=1.6.1 - ML algorithms
prometheus-client>=0.22.0 - Prometheus integration
aiohttp>=3.12.2 - Async HTTP client

Contributing

Contributions are welcome! Please read our Contributing Guide before submitting pull requests.

Security

For security vulnerabilities, please see our Security Policy.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

Built with FastMCP framework
Inspired by the needs of SRE teams managing complex Kubernetes environments

Install

Configuration

Environment variables

KUBECONFIGdefault ~/.kube/config

Path to Kubernetes config file for cluster authentication

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "lumino": {
      "command": "uvx",
      "args": [
        "lumino-mcp-server"
      ],
      "env": {
        "KUBECONFIG": "~/.kube/config"
      }
    }
  }
}

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)

Using pip

Usage

Local Mode (stdio transport)

Kubernetes Mode (HTTP streaming transport)

Usage Examples

🔍 Intelligent Root Cause Analysis

🎯 Predictive Intelligence & Forecasting

🧪 Simulation & What-If Analysis

🗺️ Topology & Dependency Mapping

🔬 Advanced Investigation & Forensics

📊 CI/CD Pipeline Intelligence

🎨 Progressive Event Analysis

🚀 Real-Time Monitoring & Alerts

🔐 Security & Compliance

📈 Advanced Analytics & ML Insights

Configuration

Kubernetes Authentication

Environment Variables

Available Tools

Kubernetes Core (5 tools)

Tekton Pipelines (6 tools)

Log Analysis (6 tools)

Event Analysis (3 tools)

Failure Analysis & RCA (2 tools)

Resource Monitoring (4 tools)

Namespace Investigation (2 tools)

Certificate & Security (2 tools)

OpenShift Specific (3 tools)

CI/CD Performance (2 tools)

Topology & Prediction (2 tools)

Simulation (1 tool)

Architecture

How It Works

Workflow

Key Features

MCP Client Integration

Method 1: Using MCPM (Recommended for Claude Code CLI / Gemini CLI)

Install MCPM

Install LUMINO MCP Server

Manage LUMINO

Method 2: Manual Configuration

Claude Desktop

Claude Code CLI

Gemini CLI

Cursor IDE

Configuration Notes

Using Alternative Python Package Managers

With pip instead of uv

With poetry

Testing Your Configuration

Advanced Configuration

Multiple Clusters

Custom Log Level

Supported Transports

Performance Considerations

Optimizing for Large Clusters

Token Budget Management

Query Optimization Tips

Performance Metrics

Caching Strategy