Large language models (LLMs) like ChatGPT and Claude have revolutionized interaction with information, yet they face isolation challenges from real-world systems. The Model Context Protocol (MCP), developed by Anthropic, offers a solution by standardizing the integration of LLMs with external data sources and tools, thereby overcoming the "NxM problem" where numerous LLMs and tools require individual integration efforts. MCP simplifies development by providing a universal protocol for AI applications to interact with external systems, building on function calling to streamline tool specification and usage without custom integration. The architecture of MCP includes host applications, clients, servers, and a transport layer, utilizing JSON-RPC 2.0 for communication. With rapid adoption since its introduction, MCP supports various clients and servers, although security issues such as lack of authentication and over-permissioning remain challenges. The protocol is evolving with proposed enhancements like secure elicitation and progressive scoping, aiming to further standardize and secure LLM interactions with external systems, thereby reducing development overhead and fostering innovation across the AI ecosystem.