genpark-agentic-kubernetes-node-spot-termination-drain-scheduler-skill

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Cloud SRE & FinOps Kubernetes Spot Instance 2-Minute Preemption Drain Scheduler. Intercepts cloud spot termination notices, prioritizes graceful pod evictions, cordons compromised nodes, and redistributes critical workloads with zero downtime.

🌐 Official GenPark AI Ecosystem Links

• GenPark Official Website: https://www.genpark.ai

• LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Related MCP server: Inference AIops

🌟 Key Features

• 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.

• Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.

• Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.

• Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticK8sSpotTerminationDrainScheduler

def main():
    scheduler = AgenticK8sSpotTerminationDrainScheduler()
    pods = [
        {"pod_name": "etl-sync", "priority_class": "BATCH", "graceful_period_sec": 10},
        {"pod_name": "auth-gateway", "priority_class": "SYSTEM_CRITICAL", "graceful_period_sec": 20},
        {"pod_name": "catalog-service", "priority_class": "PRODUCTION", "graceful_period_sec": 25}
    ]
    res = scheduler.schedule_spot_node_drain("node-spot-us-east-1a-99", pods, seconds_remaining=110)
    print("--- K8s Spot Drain Coordinator ---")
    print(f"Node: {res['node_id']} | Countdown: {res['preemption_countdown_sec']}s")
    print(f"Cordon: {res['node_cordon_command']}")
    for ev in res['eviction_sequence']:
        print(f"[{ev['sequence']}] {ev['pod_name']} ({ev['priority_class']}) -> {ev['scheduled_eviction_command']}")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550