PTP MCP Server

# PTP MCP Server A Model Context Protocol (MCP) server for monitoring and analyzing Precision Time Protocol (PTP) systems in OpenShift clusters. ## 🚀 Features - **PTP Configuration Analysis**: Parse and validate PTP configurations from OpenShift - **Real-time Log Monitoring**: Access linuxptp daemon logs with intelligent parsing - **Natural Language Queries**: Ask questions about PTP status in plain English - **Health Monitoring**: Comprehensive PTP system health checks - **Synchronization Analysis**: Monitor sync status, offsets, and BMCA state - **Clock Hierarchy**: Track grandmaster and clock hierarchy information - **ITU-T Compliance**: Validate configurations against ITU-T G.8275.1 standards ## 📋 Prerequisites - Python 3.8 or higher - OpenShift CLI (`oc`) installed and configured - Access to OpenShift cluster with PTP operator installed - PTP namespace (`openshift-ptp`) exists ## 🛠️ Installation 1. **Clone the repository**: ```bash git clone https://github.com/aneeshkp/ptp-mcp-server.git cd ptp-mcp-server ``` 2. **Install dependencies**: ```bash pip install -r requirements.txt ``` 3. **Verify OpenShift access**: ```bash oc whoami oc get namespace openshift-ptp ``` ## 🧪 Quick Testing Run the comprehensive test suite: ```bash python quick_test.py ``` Expected output: ``` 🔍 PTP MCP Server API Quick Test ================================================== Tests Passed: 8/8 Success Rate: 100.0% 🎉 ALL TESTS PASSED! Your API is ready for agent integration. ``` ## 📚 API Endpoints ### 1. Configuration API ```python from ptp_tools import PTPTools tools = PTPTools() result = await tools.get_ptp_config({"namespace": "openshift-ptp"}) ``` ### 2. Logs API ```python result = await tools.get_ptp_logs({"lines": 1000}) ``` ### 3. Search API ```python result = await tools.search_logs({"query": "dpll", "time_range": "last_hour"}) ``` ### 4. Health API ```python result = await tools.check_ptp_health({"check_config": True, "check_sync": True}) ``` ### 5. Natural Language API ```python result = await tools.query_ptp({"question": "What is the current grandmaster?"}) ``` ### 6. Grandmaster Status API ```python result = await tools.get_grandmaster_status({"detailed": True}) ``` ### 7. Sync Status API ```python result = await tools.analyze_sync_status({"include_offsets": True}) ``` ### 8. Clock Hierarchy API ```python result = await tools.get_clock_hierarchy({"include_ports": True}) ``` ## 🚀 Usage Examples ### Basic Health Check ```python import asyncio from ptp_tools import PTPTools async def check_health(): tools = PTPTools() health = await tools.check_ptp_health({}) if health["success"]: print(f"Status: {health['overall_status']}") for check_name, result in health["checks"].items(): print(f"{check_name}: {result}") else: print(f"Error: {health.get('error')}") asyncio.run(check_health()) ``` ### Natural Language Query ```python async def ask_question(): tools = PTPTools() response = await tools.query_ptp({ "question": "What is the current grandmaster?" }) if response["success"]: print(f"Answer: {response['response']}") else: print(f"Error: {response.get('error')}") asyncio.run(ask_question()) ``` ### Log Analysis ```python async def analyze_logs(): tools = PTPTools() # Get recent logs logs = await tools.get_ptp_logs({"lines": 500}) # Search for specific events sync_loss = await tools.search_logs({"query": "sync loss"}) clock_changes = await tools.search_logs({"query": "clockClass change"}) print(f"Total logs: {logs['logs_count']}") print(f"Sync loss events: {sync_loss['matching_logs']}") print(f"Clock changes: {clock_changes['matching_logs']}") asyncio.run(analyze_logs()) ``` ## 🔧 MCP Server Start the MCP server for integration with MCP-compatible clients: ```bash python ptp_mcp_server.py ``` The server provides the following MCP tools: - `get_ptp_config` - Get PTP configuration - `get_ptp_logs` - Get linuxptp daemon logs - `search_logs` - Search logs for patterns - `get_grandmaster_status` - Get grandmaster info - `analyze_sync_status` - Analyze sync status - `get_clock_hierarchy` - Get clock hierarchy - `check_ptp_health` - Comprehensive health check - `query_ptp` - Natural language interface ## 📊 Performance - **Average Response Time**: 0.78s - **Fastest API**: Configuration API (0.22s) - **Concurrent Operations**: 4/4 successful in 2.45s - **Success Rate**: 100% (8/8 endpoints) ## 🏗️ Architecture ``` ptp-mcp-server/ ├── ptp_mcp_server.py # Main MCP server ├── ptp_config_parser.py # PTP configuration parser ├── ptp_log_parser.py # Linuxptp log parser ├── ptp_model.py # PTP data models ├── ptp_query_engine.py # Natural language query engine ├── ptp_tools.py # API endpoint implementations ├── quick_test.py # Quick test suite ├── performance_test.py # Performance benchmarking └── requirements.txt # Python dependencies ``` ## 🔍 PTP Concepts Supported - **BMCA (Best Master Clock Algorithm)**: Clock selection and hierarchy - **Clock Types**: OC (Ordinary Clock), BC (Boundary Clock), TC (Transparent Clock) - **ITU-T G.8275.1**: Profile compliance and validation - **Synchronization**: Offset tracking, frequency adjustment, sync status - **Grandmaster**: Primary time source identification and status - **Clock Class**: Quality and traceability indicators - **Domain Numbers**: PTP domain configuration (24-43 for ITU-T) ## 🧪 Testing ### Run All Tests ```bash python quick_test.py ``` ### Performance Testing ```bash python performance_test.py ``` ### Individual Component Testing ```bash # Test configuration parser python -c "from ptp_config_parser import PTPConfigParser; import asyncio; asyncio.run(PTPConfigParser().get_ptp_configs())" # Test log parser python -c "from ptp_log_parser import PTPLogParser; import asyncio; asyncio.run(PTPLogParser().get_ptp_logs())" ``` ## 📖 Documentation - [Testing Guide](testing_guide.md) - Comprehensive testing instructions - [Agent Integration Guide](agent_integration_guide.md) - Integration examples for agents - [Testing Steps](TESTING_STEPS.md) - Step-by-step testing process - [Testing Results](TESTING_RESULTS.md) - Complete test results ## 🤝 Contributing 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🙏 Acknowledgments - OpenShift PTP Operator team - Linuxptp project - Model Context Protocol (MCP) community ## 📞 Support For issues and questions: - Create an issue on GitHub - Check the [testing documentation](TESTING_STEPS.md) - Review the [agent integration guide](agent_integration_guide.md) --- **Status**: ✅ Production Ready **Last Updated**: January 2025 **Version**: 1.0.0