Distributed Agent Fleet Heartbeat & Liveness Watchdog
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Distributed Agent Fleet Heartbeat & Liveness Watchdogcheck fleet liveness and reroute tasks from zombie workers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-distributed-agent-heartbeat-liveness-watchdog-skill
Distributed Agent Fleet Heartbeat & Liveness Watchdog. Monitors heartbeat signals across distributed autonomous agent nodes, dynamically computes jitter-aware failover deadlines ($T_{\text{timeout}} = \mu + 3\sigma$), isolates zombie/stalled workers, and coordinates state checkpoint hydration and failover rerouting.
🌟 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, andtools/call.Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
Dual Deployment Ecosystem: Verified across
alphaparkincandAlpha-Parkorganizations with multi-account validation.
Related MCP server: sentinel-agent-system
🚀 Quick Start
1. Direct Python SDK Usage
"""Example usage for DistributedAgentHeartbeatLivenessWatchdog."""
import sys
import json
import time
from client import DistributedAgentHeartbeatLivenessWatchdog
sys.stdout.reconfigure(encoding='utf-8')
def main():
print("=== Distributed Agent Fleet Liveness & Failover Watchdog Demo ===")
watchdog = DistributedAgentHeartbeatLivenessWatchdog(baseline_timeout_seconds=5.0)
# 1. Register fleet workers with regular heartbeat pulses
print("\n--- 1. Ingesting Agent Heartbeat Pulses ---")
watchdog.record_heartbeat("workbuddy_worker_01", "HEALTHY", cpu_load_pct=22.5, active_task_id="TASK-EXPORT-PPT")
watchdog.record_heartbeat("workbuddy_worker_02", "HEALTHY", cpu_load_pct=34.1, active_task_id="TASK-AUDIT-MERKLE")
print(f"Registered {len(watchdog.workers)} active fleet workers.")
# 2. Audit fleet health (all operational)
print("\n--- 2. Auditing Fleet Health Status ---")
health = watchdog.audit_fleet_health()
print(f"Fleet Status: {health['fleet_status']} (Healthy: {health['healthy_workers_count']}/{health['total_workers']})")
# 3. Simulate sudden unresponsiveness on worker 01
print("\n--- 3. Simulating Node Timeout / Zombie Worker ---")
watchdog.workers["workbuddy_worker_01"]["last_heartbeat"] = time.time() - 15.0
degraded_health = watchdog.audit_fleet_health()
print(f"Fleet Status: {degraded_health['fleet_status']} | Stalled Workers: {degraded_health['zombie_workers_count']}")
for z in degraded_health["zombie_workers"]:
print(f" [Zombie Alert]: Node @{z['agent_id']} stalled for {z['elapsed_since_heartbeat_seconds']}s (Task: {z['stalled_task_id']})")
# 4. Trigger automated failover task migration
print("\n--- 4. Executing Failover Lease Migration ---")
failover = watchdog.trigger_worker_failover("workbuddy_worker_01", backup_agent_id="workbuddy_worker_02")
print(f"Failover Token: {failover['failover_token']}")
print(f"Migrated Task '{failover['migrated_task_id']}' to Worker @{failover['assigned_backup_agent_id']}")
if __name__ == "__main__":
main()
2. Run as Model Context Protocol (MCP) Server
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute 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
Licensed under the MIT License. Copyright © 2026 GenPark AI.
This server cannot be deployed
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