genpark-ctrip-itinerary-dag-optimizer-skill

Executive Overview & Architectural Motivation

In the era of vertical foundation models, Ctrip / Trip.com Group pioneered the travel industry's vertical intelligence platform (Ctrip Quest / 携程问道) and multi-agent service ecosystem (TripGenie & Corporate Travel Agents). Travel planning inherently presents multi-dimensional combinatorial NP-hard challenges: volatile flight yield curves, fluctuating hotel price parities, international cross-border visa jurisdictions, and peak attraction crowd bottlenecks.

This skill library distills the core mathematical logic and agent orchestration models powering the next-generation GenPark Ctrip AI Travel Agent OS:

• Combinatorial Itinerary Optimization: Dynamic TSP graph solver with fatigue and opening hour constraints.
• Yield Management & Price Forecasting: Probabilistic expected-value airline pricing hazard models.
• Cross-Surface Compliance & Verification: Cryptographic policy checks and zero-friction corporate expense auditing.
• Enterprise Protocol Parity: Full native Model Context Protocol (MCP) compliance for agentic runtime integration.

Architecture Diagram

flowchart TD
    subgraph UserIntent["Traveler / Corporate Traveler Intent"]
        Prompt[Natural Language Travel Request] --> Parser[Multi-Modal Semantic Parser]
    end

    subgraph AgentCore["GenPark Ctrip Agent Core"]
        Parser --> CoreEngine["GenParkCtripItineraryDAGOptimizer"]
        CoreEngine --> DomainMath[Mathematical Modeling & Constraint Satisfaction]
        DomainMath --> OutputPayload[Optimized Schedule / Decision Payload / MCP Response]
    end

    subgraph SupplyChain["Ctrip Global Supply Chain & Inventory"]
        OutputPayload --> GlobalGDS[Flights, High-Speed Rail & Hotels]
        OutputPayload --> LocalServices[Visas, Attractions & Ground Transfers]
    end

Technical Specifications & Features

Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Standalone MCP Server Launch

Run the native STDIO MCP server for Claude Desktop, Cursor, or Antigravity integration:

python mcp_server.py

3. Model Context Protocol (MCP) Config

Add to your claude_desktop_config.json or agent runner config:

{
  "mcpServers": {
    "genpark-ctrip-itinerary-dag-optimizer-skill": {
      "command": "python",
      "args": ["path/to/genpark-ctrip-itinerary-dag-optimizer-skill/mcp_server.py"]
    }
  }
}

Frequently Asked Questions (FAQ)

Q1: What makes GenPark Ctrip AI Travel Agent Skills unique?

Engineered with zero external dependencies using pure Python standard library code. This ensures instantaneous cold starts, sub-millisecond execution overhead, and zero package version conflicts across diverse agent runtime environments.

Q2: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.

License

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