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

🛠️ Source Installation

1. Clone the repository:

   git clone https://github.com/redhat-cne/ptp-mcp-server.git
   cd ptp-mcp-server
   

To Deploy For Quick Tests

1. Install dependencies:

   pip install -r requirements.txt
   

2. Verify OpenShift access:

   export KUBECONFIG=/path/to/kubeconfig
   oc whoami
   oc get namespace openshift-ptp
   

3. Quick Testing

Run the comprehensive test suite:

export KUBECONFIG=/path/to/kubeconfig
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.

🔧 MCP Server

The MCP server supports two transport modes:

• stdio: For local MCP clients (Claude Code, Claude Desktop)
• HTTP/SSE: For OpenShift Lightspeed integration

Local Usage (stdio mode)

python ptp_mcp_server.py

Remote Usage (http mode)

# Default port 8080
python ptp_mcp_server.py --http

# Custom port
python ptp_mcp_server.py --http --port 9000

# Or use environment variable
PTP_MCP_PORT=9000 python ptp_mcp_server.py --http

Deploy to OpenShift

podman build -t quay.io/$USER/ptp-mcp-server:latest .
podman push quay.io/$USER/ptp-mcp-server:latest
cd k8s && kustomize edit set image quay.io/redhat-cne/ptp-mcp-server=quay.io/$USER/ptp-mcp-server:latest && cd ..
oc apply -k k8s/

Configure in OpenShift Lightspeed

Add the MCP server to your OLSConfig:

apiVersion: ols.openshift.io/v1alpha1
kind: OLSConfig
metadata:
  name: cluster
spec:
  featureGates:
  - MCPServer
  mcpServers:
  - name: ptp-monitoring
    url: 'http://ptp-mcp-server.openshift-ptp.svc.cluster.local:8080/mcp'
    timeout: 30

Usage in OpenShift Lightspeed

By default, the MCP server assumes that it is running on the cluster that is to be monitored/queried. Any tool use will target the local cluster. However, if the MCP server is running on a hub cluster and the user intends for its prompt to target spoke cluster then OLS context should include a base64 copy of the kubeconfig for the spoke cluster.

note: It is important to use a minimal kubeconfig that is token based rather than a client certificate based kubeconfig as the OLS context size will not allow for a larger kubeconfig value.

The following example generates such a kubeconfig for lab testing purposes. In a production environment care should be taken to limit the service account permissions to only the strict minimum RBAC policies required.

oc create sa ols-ptp-user -n default 2>/dev/null
oc adm policy add-cluster-role-to-user cluster-admin -z ols-ptp-user -n default
TOKEN=$(oc create token ols-ptp-user -n default --duration=24h)
API_SERVER=$(oc whoami --show-server)
cat << EOF > minimal-kubeconfig.yaml
apiVersion: v1
kind: Config
clusters:
- cluster:
    server: ${API_SERVER}
    insecure-skip-tls-verify: true
  name: cluster
contexts:
- context:
    cluster: cluster
    user: user
  name: ctx
current-context: ctx
users:
- name: user
  user:
    token: ${TOKEN}
EOF

📚 API Endpoints

1. Configuration API

from ptp_tools import PTPTools
tools = PTPTools()
result = await tools.get_ptp_config({"namespace": "openshift-ptp"})

2. Logs API

result = await tools.get_ptp_logs({"lines": 1000})

3. Search API

result = await tools.search_logs({"query": "dpll", "time_range": "last_hour"})

4. Health API

result = await tools.check_ptp_health({"check_config": True, "check_sync": True})

5. Natural Language API

result = await tools.query_ptp({"question": "What is the current grandmaster?"})

6. Grandmaster Status API

result = await tools.get_grandmaster_status({"detailed": True})

7. Sync Status API

result = await tools.analyze_sync_status({"include_offsets": True})

8. Clock Hierarchy API

result = await tools.get_clock_hierarchy({"include_ports": True})

🚀 Usage Examples

Basic Health Check

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

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

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 Tools Available

Tool	Description
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
run_pmc_query	Execute PMC commands

Deployment Files

File	Purpose
Dockerfile	Container image with Python + oc CLI
k8s/rbac.yaml	ServiceAccount, ClusterRole, ClusterRoleBinding
k8s/deployment.yaml	Deployment with health checks
k8s/service.yaml	ClusterIP Service
k8s/olsconfig-example.yaml	Example OLS configuration
k8s/kustomization.yaml	Deploy all with oc apply -k k8s/

RBAC Permissions

The ServiceAccount is granted these permissions:

Resource	Verbs	Purpose
ptpconfigs, ptpoperatorconfigs	get, list, watch	Read PTP configurations
pods	get, list, watch	Find linuxptp-daemon pods
pods/log	get, list	Read daemon logs
pods/exec	create	Execute PMC queries
namespaces	get, list	Namespace access
nodes	get, list	Node topology (optional)

📊 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 (stdio + HTTP modes)
├── 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
├── Dockerfile             # Container image definition
└── k8s/                   # Kubernetes/OpenShift manifests
    ├── kustomization.yaml # Kustomize configuration
    ├── rbac.yaml          # ServiceAccount & RBAC
    ├── deployment.yaml    # Deployment specification
    ├── service.yaml       # Service definition
    └── olsconfig-example.yaml  # OLS integration example

🔍 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

python quick_test.py

Performance Testing

python performance_test.py

Individual Component Testing

# 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 - Comprehensive testing instructions
• Agent Integration Guide - Integration examples for agents
• Testing Steps - Step-by-step testing process
• Testing Results - 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 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
• Review the agent integration guide

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Status: ✅ Production Ready
Last Updated: January 2025
Version: 1.0.0