genpark-agentic-travel-multi-leg-itinerary-synthesizer-skill
OfficialThis 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-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: Travel City 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.
Related MCP Connectors
Personal AI travel agent. Points optimization, live flight/hotel/award search, trip planning.
Trip planner your AI assistant can plan and edit: persistent trips, group plans, booking records.
Travel tools for AI agents: plan and edit real trips, search stays and tours, import travel videos.
TravelMind: 8 MCP tools for travel (12306 trains, flights, hotels, geocode, planning, policy).
Related MCP Servers
- AlicenseAqualityDmaintenanceEnables AI agents to plan trips by searching flights, accommodations, and activities, and managing itineraries collaboratively with role-based permissions.2019 npm112 PyPI1MIT
- FlicenseNot gradedqualityDmaintenanceConverts natural language travel requests into customizable multi-city itineraries, with tools for planning, editing, and exporting trips across 69 global destinations.-
- FlicenseNot gradedqualityCmaintenanceEnables multi-agent travel planning by orchestrating specialized agents for transport, accommodation, and experiences, using live web search and structured data extraction to generate complete trip plans.-
- AlicenseNot gradedqualityCmaintenanceEnables turning natural-language vacation or business travel goals into multi-leg flight segments, lodging choices, layover buffers, baggage fee optimization, and consolidated reservation manifests via MCP.MIT