The Model Context Protocol (MCP): Standardizing Agentic Interoperability

Abstract

The transition of Large Language Models (LLMs) from static text generation to "agentic" workflow execution has exposed a critical infrastructure deficit: the "N × M" integration problem, where connecting $N$ models to $M$ data sources requires bespoke connectors. The Model Context Protocol (MCP), an open standard introduced in late 2024, addresses this by standardizing the interface between AI models (hosts) and external data/tools (servers). This paper analyzes the technical architecture of MCP and its transformative economic impact, future path for Agent to Agent (A2A) Communication and agent framework . We present empirical data demonstrating significant productivity gains, including a 50% acceleration in AI deployment timelines, a 25% reduction in diagnostic errors in healthcare, and a 98% reduction in token overhead for code execution tasks. Furthermore, we rigorously examine the security landscape, detailing mitigation strategies for "Confused Deputy" attacks through OAuth 2.0 and RBAC, and align MCP deployment with frameworks like the NIST AI RMF and CJIS Security Policy.