Model Context Protocol for Agentic AI: Enabling Contextual Interoperability Across Systems

Abstract

The Model Context Protocol (MCP) is an open standard developed by Anthropic to facilitate seamless integration between large language models (LLMs) and external data sources or tools. By standardizing context exchange, MCP allows AI agents to interact with diverse systems in a secure and scalable manner. This paper explores MCP's architecture, components, and its role in enhancing agentic AI capabilities.The protocol introduces primitives such as tools, resources, prompts, and sampling to establish a structured interaction layer between models and environments. MCP’s modular design supports real-time, bidirectional communication over common transports like HTTP and WebSocket. Its adoption has enabled AI systems to become more flexible, interactive, and autonomous in enterprise and developer workflows. As context-aware intelligence becomes foundational, MCP offers a scalable pathway for building the next generation of agentic applications