Autonomous AI Fleet Governance Circuit Breaker
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-autonomous-ai-fleet-governance-circuit-breaker-skill
Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)
Autonomous Multi-Agent Fleet Governance & Blast-Radius Circuit Breaker. Century Milestone Master Controller! Monitors distributed agent token burn velocity, recursive subagent spawning cascades, cyclic deadlocks, and enforces system-wide kill-switch containment policies with cryptographically verifiable audit tokens.
🌐 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: @vaibot/mcp-server
🌟 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.
🚀 Quick Start
1. Direct Python SDK Usage
from client import AutonomousAIFleetGovernanceCircuitBreaker
def main():
controller = AutonomousAIFleetGovernanceCircuitBreaker()
telemetry = {
"active_agents_count": 8,
"max_spawn_depth": 3,
"total_tokens_per_min": 65000,
"cost_burn_rate_usd_hour": 18.5
}
res = controller.evaluate_fleet_governance(telemetry)
print("--- Century Fleet Governance Monitor ---")
print(f"Breaker Status: {res['circuit_breaker_state']}")
print(f"Kill Switch Active: {res['kill_switch_active']}")
print(f"Metrics: {res['fleet_metrics']}")
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 & 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
Related MCP Connectors
Governed MCP: agent audit, provenance, deterministic checks, and receipt-backed FragGate execution.
Control plane for autonomous software labor. Agents claim objectives over MCP with audit trail.
MCP gateway with runtime security policy, tool-call-level control, and audit of agent actions.
Monitoring for the agent economy — liveness, latency, trust scoring for MCP endpoints
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to self-audit their token usage and costs, providing burn reports, forensics, and optimization recommendations via MCP.65 PyPI113MIT

@vaibot/mcp-serverofficial
AlicenseAqualityDmaintenanceGovernance circuit-breaker MCP server that enables AI agents to request risk-based decisions, approve or deny actions, and finalize outcomes with full audit receipts.4MIT- AlicenseNot gradedqualityBmaintenanceGoverns AI agents' spending by enforcing budgets, approvals, and kill switches before any payment, providing an MCP interface for tool calls with policy checks.377 npmApache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables AI agents and MCP servers to operate under autonomous security enforcement, including pre-deployment scanning, per-call authorization, runtime monitoring, incident containment, and comprehensive auditing.1MIT