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OpenShift OVN-Kubernetes Benchmark MCP Server

by liqcui
OverviewInspectNewSchemaRelated ServersScore
Python
Remote

OpenShift OVN-Kubernetes Benchmark MCP Server

A comprehensive benchmarking and performance monitoring solution for OpenShift clusters using OVN-Kubernetes networking, built with FastMCP and AI-powered analysis.

Architecture Overview

Topology

Web UI

Features

🔧 Core Capabilities

  • Automated Authentication: Discovers and authenticates with OpenShift/Kubernetes clusters

  • Multi-Source Monitoring: Collects metrics from Prometheus, Kubernetes API, and cluster resources

  • AI-Powered Analysis: Uses LangGraph and OpenAI for intelligent insights and recommendations

  • Comprehensive Reporting: Generates Excel and PDF reports with visualizations

  • Historical Tracking: Stores performance data in DuckDB for trend analysis

📊 Monitored Components

  • Kubernetes API Server: Request latency, throughput, and error rates

  • Multus CNI: Resource usage and pod networking performance

  • OVN-Kubernetes Pods: Control plane and node performance

  • OVN Containers: Database sizes, memory usage, and sync performance

  • OVS Components: CPU and memory usage of OVS processes

  • General Cluster Info: NetworkPolicies, AdminNetworkPolicies, EgressFirewalls

🤖 AI Features

  • Automated performance trend analysis

  • Intelligent alert correlation

  • Proactive recommendations

  • Risk assessment and health scoring

  • Natural language insights

Quick Start

Prerequisites

  • Python 3.9+

  • OpenShift/Kubernetes cluster access

  • KUBECONFIG file

  • OpenAI API key (for AI features)

Installation

  1. Clone and Setup

    git clone <repository> cd ocp-benchmark-mcp chmod +x ovnk_benchmark_mcp_command.sh ./ovnk_benchmark_mcp_command.sh setup

  2. Test Configuration

    ./ovnk_benchmark_mcp_command.sh -k ~/.kube/config test

Usage

Start MCP Server

# Start server (runs on port 8000) ./ovnk_benchmark_mcp_command.sh -k ~/.kube/config server

Collect Performance Data

# Collect data for 10 minutes ./ovnk_benchmark_mcp_command.sh -k ~/.kube/config -d 10m collect

Generate Reports

# Generate report for last 7 days ./ovnk_benchmark_mcp_command.sh -o sk-your-openai-key -p 7 report

Full Workflow

# Collect data and generate report ./ovnk_benchmark_mcp_command.sh -k ~/.kube/config -o sk-your-openai-key full

Project Structure

ocp-benchmark-mcp/ ├── README.md # This file ├── pyproject.toml # Python project configuration ├── ovnk_benchmark_mcp_server.py # Main MCP server ├── ovnk_benchmark_mcp_agent_perfdata.py # Data collection agent ├── ovnk_benchmark_mcp_agent_report.py # Report generation agent ├── ovnk_benchmark_mcp_command.sh # Startup script ├── ocauth/ │ └── ovnk_benchmark_auth.py # OpenShift authentication ├── tools/ │ ├── ovnk_benchmark_openshift_generalinfo.py # Cluster general info │ ├── ovnk_benchmark_prometheus_basequery.py # Base Prometheus queries │ ├── ovnk_benchmark_prometheus_kubeapi.py # API server metrics │ ├── ovnk_benchmark_prometheus_multus.py # Multus CNI metrics │ ├── ovnk_benchmark_prometheus_ovnk_pods.py # OVN-K pod metrics │ ├── ovnk_benchmark_prometheus_ovnk_containers.py # OVN container metrics │ └── ovnk_benchmark_prometheus_ovnk_sync.py # OVN sync metrics ├── config/ │ ├── ovnk_benchmark_config.py # Configuration management │ └── metrics-base.yml # Prometheus metrics definitions ├── analysis/ │ └── ovnk_benchmark_performance_analysis.py # Performance analysis ├── elt/ │ └── ovnk_benchmark_elt_duckdb.py # Data processing ├── storage/ │ └── ovnk_benchmark_prometheus_ovnk.py # DuckDB storage └── exports/ # Generated reports

Configuration

Environment Variables

Variable

Description

Default

KUBECONFIG

Path to kubeconfig file

~/.kube/config

OPENAI_API_KEY

OpenAI API key for AI features

Required for reports

MCP_SERVER_URL

MCP server URL

http://localhost:8000

COLLECTION_DURATION

Metrics collection duration

5m

REPORT_PERIOD_DAYS

Report period in days

7

DATABASE_PATH

DuckDB database path

storage/ovnk_benchmark.db

REPORT_OUTPUT_DIR

Report output directory

exports

export OVNK_PROMETHEUS_USE_ROUTE=true # Use OpenShift routes (default)

export OVNK_PROMETHEUS_NAMESPACE=openshift-monitoring

export OVNK_PROMETHEUS_SA=prometheus-k8s

export KUBECONFIG=/path/to/kubeconfig

Metrics Configuration

The config/metrics-base.yml file defines all Prometheus queries organized by category:

  • General Information: Pod and namespace status

  • API Server: Request latency and error rates

  • Multus: CNI resource usage

  • OVN Control Plane/Node: CPU and memory metrics

  • OVN Containers: Database and controller metrics

  • OVS Containers: OVS daemon metrics

  • OVN Sync: Synchronization duration metrics

API Reference

MCP Tools

The server exposes the following MCP tools:

get_openshift_general_info

Get general cluster information including NetworkPolicy, AdminNetworkPolicy, and EgressFirewall counts.

Parameters:

  • namespace (optional): Specific namespace to query

Response:

{ "timestamp": "2024-01-01T00:00:00Z", "summary": { "total_networkpolicies": 10, "total_adminnetworkpolicies": 2, "total_egressfirewalls": 5, "total_namespaces": 25, "total_nodes": 6 } }

query_kube_api_metrics

Query Kubernetes API server performance metrics.

Parameters:

  • duration (optional): Query duration (default: "5m")

  • start_time (optional): Start time in ISO format

  • end_time (optional): End time in ISO format

query_multus_metrics

Query Multus CNI performance metrics.

query_ovnk_pods_metrics

Query OVN-Kubernetes pod performance metrics.

query_ovnk_containers_metrics

Query OVN-Kubernetes container metrics.

query_ovnk_sync_metrics

Query OVN-Kubernetes synchronization metrics.

store_performance_data

Store performance data in DuckDB.

get_performance_history

Retrieve historical performance data.

AI Agents

Performance Data Collection Agent

Uses LangGraph to orchestrate data collection:

  1. Initialize: Setup collection parameters

  2. Collect General Info: Gather cluster information

  3. Collect Metrics: Query each component category

  4. Store Data: Save to DuckDB storage

  5. Finalize: Generate collection summary

Report Generation Agent

Uses LangGraph with AI analysis:

  1. Fetch Historical Data: Retrieve performance history

  2. Analyze Performance: Calculate trends and statistics

  3. Generate Insights: Use AI for recommendations

  4. Create Reports: Generate Excel and PDF reports

  5. Finalize: Output summary and files

Storage Schema

DuckDB Tables

  • metrics: Individual metric data points

  • metric_summaries: Category performance summaries

  • performance_snapshots: Complete performance snapshots

  • benchmark_runs: Benchmark execution records

  • alerts_history: Historical alert data

Report Types

Excel Reports

  • Executive Summary: Key performance indicators

  • Historical Trends: Time-series performance data

  • Category Analysis: Component-specific metrics

  • Recommendations: AI-generated insights

  • Raw Data: Complete dataset

PDF Reports

  • Executive Summary: High-level performance overview

  • Key Metrics: Performance indicator tables

  • Category Analysis: Component performance breakdown

  • Recommendations: Prioritized action items

Troubleshooting

Common Issues

Authentication Problems

# Test cluster connectivity kubectl cluster-info # Verify kubeconfig export KUBECONFIG=/path/to/config ./ovnk_benchmark_mcp_command.sh test

Prometheus Discovery

# Check Prometheus pods kubectl get pods -n openshift-monitoring | grep prometheus # Verify service accounts kubectl get sa -n openshift-monitoring

MCP Server Issues

# Check server logs tail -f logs/mcp_server_*.log # Test server connectivity curl http://localhost:8000/health

Debug Mode

Enable debug logging:

export LOG_LEVEL=DEBUG export OVNK_LOG_LEVEL=DEBUG ./ovnk_benchmark_mcp_command.sh server

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Add tests for new functionality

  4. Submit a pull request

Development Setup

# Install development dependencies pip install -e .[dev] # Run tests pytest # Format code black . # Type checking mypy .

License

MIT License - see LICENSE file for details.

Support

For issues and questions:

  1. Check the troubleshooting section

  2. Review logs in the

  3. Open an issue with detailed logs and configuration

Roadmap

  • Kubernetes native deployment (Helm charts)

  • Grafana dashboard integration

  • Custom alert rule definitions

  • Multi-cluster support

  • Real-time streaming metrics

  • Advanced ML-based anomaly detection

  • Integration with CI/CD pipelines

Note: This tool is designed for OpenShift clusters with OVN-Kubernetes networking. Some features may not be available on other Kubernetes distributions.

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