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Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications
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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.
Forward citations
Cited by 10 Pith papers
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Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models
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An LLM-powered digital twin uses MCP to connect to Gurobi and AnyLogic, automating freight optimization workflows from natural language requests, but the evidence is limited to one 14-node case study.
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Towards Humanoid Robot Autonomy: A Dynamic Architecture Integrating Continuous thought Machines (CTM) and Model Context Protocol (MCP)
A proposed CTM-MCP architecture for humanoid robot autonomy is supported only by self-assessed LLM simulations, not by real robots or independent benchmarks.
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Model Context Protocols in Adaptive Transport Systems: A Survey
The paper proposes MCP as the unifying standard for context exchange in adaptive transport systems, based on a five-category taxonomy of prior work and a speculative convergence claim.
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Contextual Memory Intelligence reframes memory as dynamic infrastructure and proposes the Insight Layer to preserve decision rationale, detect semantic drift, and support human-in-the-loop reflection.
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A qualitative taxonomy positions vibe coding and agentic coding as complementary paradigms rather than rivals in AI-assisted software development.
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Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
ISEK is a conceptual blockchain-and-token architecture for coordinating human and AI agents, with no implementation, experiments, or formal results reported.
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