The New Interoperability Paradigm: Model Context Protocol (MCP), APIs, and the Future of Agentic AI

Abstract

The rise of agentic artificial intelligence has exposed a critical weakness in modern digital ecosystems, the lack of a unified, context-aware interoperability layer capable of connecting autonomous reasoning systems with the governed, deterministic world of enterprise APIs. Existing integration models  REST, GraphQL, and proprietary plug-ins  remain fundamentally stateless and syntactic, making them incapable of preserving semantic continuity, negotiating schemas dynamically, or supporting multi-agent collaboration at scale. This paper introduces the Model Context Protocol (MCP) as a new interoperability paradigm that bridges this gap by enabling persistent context propagation, context-bound tool invocation, and embedded governance metadata across heterogeneous systems. By positioning MCP as the semantic complement to traditional API infrastructures, we demonstrate how it enables secure, explainable, and policy-compliant orchestration of agentic workflows. We propose an MCP API Interoperability Framework that unifies context governance, dynamic schema alignment, and multi-agent coordination, and we validate its applicability through real-world enterprise, multi-cloud, and scientific collaboration scenarios. Through comparative analysis and architectural modeling, the study shows that MCP transforms interoperability from a structural exchange of data into a semantic continuum that enables trustworthy, scalable, and auditable agentic AI. The results establish MCP as a foundational layer for the next generation of autonomous, interoperable, and enterprise-ready AI systems.