Trip Planner: An MCP-Based Multi-Agent Service Orchestration Framework for Intelligent Travel Planning

This paper presented Trip Planner using MCP, an intelligent travel planning framework that addresses interoperability and integration challenges in modern travel applications. The pro-posed system combines the Model Context Protocol (MCP), a Supervisor-Agent-based orchestration mechanism, multiple domain-specific MCP servers, and Large Language Models (LLMs) to generate personalized and budget-aware travel itineraries. The implementation demonstrates how MCP can be uti-lized as a standardized communication layer for integrating heterogeneous travel services, including flights, hotels, trains, buses, tourist attractions, and budget planning modules. By decoupling service providers from the application logic, the framework improves modularity, scalability, maintainability, and extensibility compared to traditional API-centric travel systems. Experimental evaluation indicates that the proposed archi-tecture successfully aggregates travel information from multi-ple services and generates coherent, context-aware itineraries based on user preferences and constraints. The results highlight the effectiveness of MCP in enabling dynamic service orches-tration while reducing integration complexity and improving personalization capabilities. Overall, Trip Planner using MCP demonstrates the practical applicability of MCP in real-world intelligent systems and contributes a domain-specific implementation to the growing MCP ecosystem. Future work will focus on integrating real-time travel APIs, weather-aware recommendations, enhanced security mechanisms, and advanced optimization techniques to further improve travel planning performance and user experience.

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