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LUMINO MCP Server

License Python MCP PyPI

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.

Table of Contents

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

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)

# 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

# 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:

python main.py

Kubernetes Mode (HTTP streaming transport)

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

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:

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

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

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

🎯 Predictive Intelligence & Forecasting

Anticipate problems before they impact your systems:

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

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

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

"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:

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

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

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

🗺️ Topology & Dependency Mapping

Understand system architecture and component relationships:

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

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

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

🔬 Advanced Investigation & Forensics

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

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

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

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

"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:

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

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

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

"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:

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

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

"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:

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

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

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

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

🔐 Security & Compliance

Ensure cluster security and certificate management:

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

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

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

📈 Advanced Analytics & ML Insights

Leverage machine learning for pattern detection:

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

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

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

Configuration

Kubernetes Authentication

The server automatically detects Kubernetes configuration:

  1. In-cluster config - When running inside a Kubernetes pod
  2. 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

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:

┌─────────────────────────────────────────────────────────────────┐
│                        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

  1. User Query → AI assistant receives natural language request
  2. MCP Translation → Assistant converts query to appropriate tool calls
  3. LUMINO Processing → Server executes Kubernetes/Prometheus operations
  4. Data Analysis → ML/statistical algorithms process raw data
  5. 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

# 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

# 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

# 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

  1. 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

  2. Add LUMINO configuration:

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

  1. Restart Claude Desktop

  2. 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

  1. Clone and install:
git clone https://github.com/spre-sre/lumino-mcp-server.git
cd lumino-mcp-server
uv sync  # Creates .venv with all dependencies
  1. Create .mcp.json in your project root (for project-local config) or update ~/.claude.json (for global config):
{
  "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).

  1. Verify installation:
# 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

  1. Find your config file location:

    • macOS/Linux: ~/.config/gemini/mcp_servers.json
    • Windows: %APPDATA%\gemini\mcp_servers.json

  2. Add LUMINO configuration:

{
  "mcpServers": {
    "lumino": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/lumino-mcp-server",
        "python",
        "main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}
  1. Verify installation:
# Check MCP servers
gemini mcp list

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

Cursor IDE

  1. Open Cursor Settings:

    • Press Cmd+, (macOS) or Ctrl+, (Windows/Linux)
    • Search for "MCP" or "Model Context Protocol"

  2. Add MCP Server Configuration:

In Cursor's MCP settings, add:

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

Alternative - Using Cursor's settings.json:

  1. Open Command Palette (Cmd+Shift+P or Ctrl+Shift+P)
  2. Type "Preferences: Open User Settings (JSON)"
  3. Add the MCP configuration:
{
  "mcp.servers": {
    "lumino": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/lumino-mcp-server",
        "python",
        "main.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

  1. Restart Cursor IDE

  2. 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:

# 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:

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

Using Alternative Python Package Managers

With pip instead of uv

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

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

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

With poetry

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

Testing Your Configuration

After configuring any client, test the connection:

  1. 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

  2. Test a simple query:

"List all namespaces in my Kubernetes cluster"
  1. Check server logs (if issues):
# Run server manually to see errors
cd /path/to/lumino-mcp-server
uv run python main.py

Expected output:

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

Advanced Configuration

Multiple Clusters

Configure multiple LUMINO instances for different clusters:

{
  "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

{
  "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

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

Specify Time Windows

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

Leverage Smart Tools

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

Use Progressive Analysis

✅ 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

Error: Unable to load kubeconfig

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

Permission denied for resources

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

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.

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Apache-2.0 license

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Lumino MCP Server v0.9.3 Latest
Jan 30, 2026
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