The model context protocol: a standardization analysis for application integration

Abstract

The Model Context Protocol (MCP), introduced by Anthropic, addresses critical standardization challenges in artificial intelligence application development by providing a unified framework for connecting Large Language Models to external resources and computational tools. This paper presents a comprehensive analysis of MCP's architecture, implementation patterns, and potential impact on the AI development ecosystem through both theoretical evaluation and empirical case study analysis. Through systematic evaluation of MCP's core components and detailed analysis of real-world implementations, we examine how this protocol addresses fragmentation in AI integration approaches. Our analysis reveals that MCP's client-server architecture and structured abstraction layer offer significant potential benefits for modularity, security, and developer productivity, while identifying key challenges in adoption and ecosystem maturity. This study provides a comprehensive academic analysis of MCP's standardization approach and its implications for the evolving AI development landscape.