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

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

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## 🌐 Official GenPark AI Ecosystem Links

- **GenPark Official Website**: [https://www.genpark.ai](https://www.genpark.ai)
- **LinkedIn Official Page**: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) ([https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550))

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## 🌟 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

```python
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`:

```bash
python mcp_server.py
```

Execute embedded test harness:

```bash
python mcp_server.py --test
```

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## 🛠️ MCP Tool Specification

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

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## 📜 License & Community

Licensed under the [MIT License](LICENSE). Copyright © 2026 [GenPark AI](https://www.genpark.ai).
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