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

Clone and Setup

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

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:

Initialize: Setup collection parameters
Collect General Info: Gather cluster information
Collect Metrics: Query each component category
Store Data: Save to DuckDB storage
Finalize: Generate collection summary

Report Generation Agent

Uses LangGraph with AI analysis:

Fetch Historical Data: Retrieve performance history
Analyze Performance: Calculate trends and statistics
Generate Insights: Use AI for recommendations
Create Reports: Generate Excel and PDF reports
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

Fork the repository
Create a feature branch
Add tests for new functionality
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:

Check the troubleshooting section
Review logs in the logs/ directory
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.

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

Clone and Setup

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

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:

Initialize: Setup collection parameters
Collect General Info: Gather cluster information
Collect Metrics: Query each component category
Store Data: Save to DuckDB storage
Finalize: Generate collection summary

Report Generation Agent

Uses LangGraph with AI analysis:

Fetch Historical Data: Retrieve performance history
Analyze Performance: Calculate trends and statistics
Generate Insights: Use AI for recommendations
Create Reports: Generate Excel and PDF reports
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

Fork the repository
Create a feature branch
Add tests for new functionality
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:

Check the troubleshooting section
Review logs in the logs/ directory
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.