genpark-managed-agent-fleet-lifecycle-supervisor-skill

mcp
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SUMMARY

Enterprise Managed Agent Fleet Lifecycle Supervisor (inspired by Tencent WorkBuddy & Anthropic Managed Agents). Orchestrates worker agent registration, capability-based task dispatch, continuous heartbeat telemetry, automated circuit breakers, and state checkpoint recovery.

README.md

genpark-managed-agent-fleet-lifecycle-supervisor-skill

Python 3.9+
License MIT
MCP Compatible
GenPark AI
Zero Dependencies

Production-Grade Real-Time Sensory Grounding & Enterprise Managed Agent Lifecycle Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation


🌟 Overview

genpark-managed-agent-fleet-lifecycle-supervisor-skill provides industrial-grade capabilities engineered for next-generation Personal Multimodal Agents and Enterprise Workplace Execution. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.

Enterprise Managed Agent Fleet Lifecycle Supervisor (inspired by Tencent WorkBuddy & Anthropic Managed Agents). Orchestrates worker agent registration, capability-based task dispatch, continuous heartbeat telemetry, automated circuit breakers, and state checkpoint recovery.

💡 Key Capabilities

  • Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without pip install overhead or supply-chain vulnerabilities.
  • Model Context Protocol (MCP) First: Fully compatible with Claude Desktop, Cursor, GenPark Engine, Meta Muse, Ray-Ban smart glasses, and enterprise agent runtimes.
  • Deterministic & Safe: Structured JSON schemas, cryptographic verification, rigorous boundary validation, and real-time telemetry.
  • High Concurrency & Low Latency: In-memory caching, vectorized math approximations, and robust fault-tolerant state handling.

🚀 Quickstart

1. Direct Python Usage

from client import ManagedAgentFleetLifecycleSupervisor

client = ManagedAgentFleetLifecycleSupervisor()
result = client.register_worker()
print(result)

2. Standalone MCP Server Execution

Run the MCP server via standard JSON-RPC 2.0 stdio:

python mcp_server.py

Verify standard compliance and self-tests:

python mcp_server.py --test

3. Claude Desktop / Cursor MCP Configuration

Add this tool to your claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "genpark-managed-agent-fleet-lifecycle-supervisor-skill": {
      "command": "python",
      "args": ["/absolute/path/to/genpark-managed-agent-fleet-lifecycle-supervisor-skill/mcp_server.py"]
    }
  }
}

🛠️ Verification & Testing

Run the included verification suite:

python example_usage.py

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.

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