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With the rapid growth of Large Language Models (LLMs), deep learning models trained on massive datasets have demonstrated impressive capabilities in understanding human language and generating diverse responses. However, current LLMs are mainly limited to static question-answering tasks and cannot interact dynamically with external information systems. This study proposes creating an intelligent chatbot system that integrates LLMs with the Model Context Protocol (MCP). This system offers features such as real-time generation of personalized travel itineraries, assistance with booking services like hotels and restaurants, cross-lingual interactions, and intelligent recommendations. By utilizing MCP’s modular context tracking and task-oriented workflow architecture, the system is designed to handle complex user scenarios and deliver personalized travel services.

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Intelligent Chatbot System that Integrates Large Language Models with the Model Context Protocol

  • 会议论文

  • 发表日期：

    2026年

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  • Davin Valerian,
  • Ivanno Winoto,
  • Yiwei Ma,
  • Kazuya Tsukamoto,
  • Chenglong Shao

摘要

With the rapid growth of Large Language Models (LLMs), deep learning models trained on massive datasets have demonstrated impressive capabilities in understanding human language and generating diverse responses. However, current LLMs are mainly limited to static question-answering tasks and cannot interact dynamically with external information systems. This study proposes creating an intelligent chatbot system that integrates LLMs with the Model Context Protocol (MCP). This system offers features such as real-time generation of personalized travel itineraries, assistance with booking services like hotels and restaurants, cross-lingual interactions, and intelligent recommendations. By utilizing MCP’s modular context tracking and task-oriented workflow architecture, the system is designed to handle complex user scenarios and deliver personalized travel services.