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OpenShift OVN-Kubernetes Benchmark MCP Server

by liqcui
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Python
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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.

This server cannot be installed

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security - not tested
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license - not found
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quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables comprehensive benchmarking and performance monitoring of OpenShift clusters using OVN-Kubernetes networking through automated data collection, AI-powered analysis, and report generation. Provides intelligent insights into cluster performance, bottleneck detection, and optimization recommendations.

  1. Architecture Overview
    1. Topology
      1. Web UI
        1. Features
          1. 🔧 Core Capabilities
          2. 📊 Monitored Components
          3. 🤖 AI Features
        2. Quick Start
          1. Prerequisites
          2. Installation
          3. Usage
        3. Project Structure
          1. Configuration
            1. Environment Variables
            2. Metrics Configuration
          2. API Reference
            1. MCP Tools
          3. AI Agents
            1. Performance Data Collection Agent
            2. Report Generation Agent
          4. Storage Schema
            1. DuckDB Tables
          5. Report Types
            1. Excel Reports
            2. PDF Reports
          6. Troubleshooting
            1. Common Issues
            2. Debug Mode
          7. Contributing
            1. Development Setup
          8. License
            1. Support
              1. Roadmap

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