genpark-workplace-goal-to-artifact-dag-orchestrator-skill
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-workplace-goal-to-artifact-dag-orchestrator-skillBreak down Q4 revenue goal into tasks and create a financial spreadsheet and executive deck."
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-workplace-goal-to-artifact-dag-orchestrator-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-workplace-goal-to-artifact-dag-orchestrator-skill delivers robust, industrial-grade capabilities bridging Consumer Agentic Commerce 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 Work Agent Goal-to-Artifact DAG Pipeline (inspired by WorkBuddy, Tencent Docs, WeChat Work Multi-Agent). Decomposes high-level business objectives into topological sub-tasks and directly synthesizes final deliverables: financial spreadsheets, executive presentations, and analytical reports.
💡 Key Capabilities
Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without
pip installoverhead or supply-chain vulnerabilities.Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, Meta Muse, and enterprise work agent frameworks.
Deterministic & Safe: Designed with cryptographic authorization tokens, role-based boundary validation, and structured telemetry.
High Concurrency & Low Latency: In-memory caching, transactional validation, and optimized execution loops.
Related MCP server: fushiguro-mcp
🚀 Quickstart
1. Direct Python Usage
from client import WorkplaceGoalToArtifactDAGOrchestrator
client = WorkplaceGoalToArtifactDAGOrchestrator()
result = client.decompose_goal_to_dag()
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-workplace-goal-to-artifact-dag-orchestrator-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-workplace-goal-to-artifact-dag-orchestrator-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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