Model Context Protocol (MCP) is introduced as an AI-native integration standard designed to streamline the interaction between AI systems, such as Large Language Models (LLMs), and various enterprise systems by providing a common language and uniform interface to describe external tools, functions, APIs, and datasets. Emerging in late 2024, MCP builds on existing protocols like API, SOAP, and REST but is intended for machine reasoning rather than human developers. This protocol enhances the capability of AI systems to discover, reason, and orchestrate tasks by offering semantic richness and composability, allowing LLMs to understand the context and purpose of different operations. MCP contrasts with traditional APIs by providing more flexible and context-aware interactions, enabling AI agents to make real-time decisions about tool usage without manual coding. It also addresses pitfalls such as poor design and resource management, emphasizing the need for clear and comprehensive documentation, and introduces new testing and monitoring strategies to manage the variability and cost associated with AI operations. By incorporating MCP into their data strategies, enterprises can enhance integration efficiency, transparency, and adaptability, ultimately fostering a more robust AI ecosystem.