genpark-agentic-travel-multi-leg-itinerary-synthesizer-skill
OfficialClick 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-agentic-travel-multi-leg-itinerary-synthesizer-skillBook a 3-leg business trip SFO to Berlin to Dubai with hotels and bags."
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-agentic-travel-multi-leg-itinerary-synthesizer-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-agentic-travel-multi-leg-itinerary-synthesizer-skill delivers robust, industrial-grade capabilities engineered for Consumer Agentic Commerce (e.g. Meta Muse, Expedia, Mastercard Agent Connect) and Enterprise Workplace Execution (e.g. Tencent WorkBuddy Goal-to-PPT/Excel deliverable workflows). 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.
Universal Agentic Travel & Multi-Leg Hospitality Itinerary Synthesizer (inspired by Meta Muse, Expedia, Booking). Translates natural language vacation and business travel goals into multi-leg flight segments, lodging selection, layover time buffers, baggage fee optimization, and consolidated reservation manifests.
💡 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 managed agent runtimes.
Deterministic & Safe: Rigorous state machine models, cryptographic ledger hashes, mathematical dependency graphs, and full telemetry.
High Concurrency & Low Latency: In-memory caching, topological cycle detection, and optimized execution loops.
Related MCP server: trip-planner-mcp
🚀 Quickstart
1. Direct Python Usage
from client import AgenticTravelItinerarySynthesizer
client = AgenticTravelItinerarySynthesizer()
result = client.synthesize_itinerary()
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-agentic-travel-multi-leg-itinerary-synthesizer-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-agentic-travel-multi-leg-itinerary-synthesizer-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
Maintenance
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