Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications

• URL: http://arxiv.org/abs/2504.21030v1
• Date: Sat, 26 Apr 2025 03:43:03 GMT
• Title: Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications
• Authors: Naveen Krishnan,
• Abstract summary: This paper introduces a comprehensive framework for advancing multi-agent systems through Model Context Protocol (MCP)<n>We extend previous work on AI agent architectures by developing a unified theoretical foundation, advanced context management techniques, and scalable coordination patterns.<n>We identify current limitations, emerging research opportunities, and potential transformative applications across industries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-agent systems represent a significant advancement in artificial intelligence, enabling complex problem-solving through coordinated specialized agents. However, these systems face fundamental challenges in context management, coordination efficiency, and scalable operation. This paper introduces a comprehensive framework for advancing multi-agent systems through Model Context Protocol (MCP), addressing these challenges through standardized context sharing and coordination mechanisms. We extend previous work on AI agent architectures by developing a unified theoretical foundation, advanced context management techniques, and scalable coordination patterns. Through detailed implementation case studies across enterprise knowledge management, collaborative research, and distributed problem-solving domains, we demonstrate significant performance improvements compared to traditional approaches. Our evaluation methodology provides a systematic assessment framework with benchmark tasks and datasets specifically designed for multi-agent systems. We identify current limitations, emerging research opportunities, and potential transformative applications across industries. This work contributes to the evolution of more capable, collaborative, and context-aware artificial intelligence systems that can effectively address complex real-world challenges.

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