genpark-managed-agent-fleet-lifecycle-supervisor-skill
OfficialProvides agent fleet lifecycle supervision and real-time sensory grounding capabilities for Meta Muse and Ray-Ban smart glasses environments, enabling worker registration, task dispatch, and telemetry.
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., "@genpark-managed-agent-fleet-lifecycle-supervisor-skillregister a vision worker, start its heartbeat, and dispatch a recovery task"
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-managed-agent-fleet-lifecycle-supervisor-skill
🌐 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 installoverhead 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.
Related MCP server: polyflow
🚀 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.pyVerify standard compliance and self-tests:
python mcp_server.py --test3. 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.
This server cannot be deployed
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