The article outlines the Model Context Protocol (MCP), a framework that enhances the integration of Large Language Models (LLMs) with external tools and data sources. MCP aims to eliminate information silos by providing a consistent interface, making it easier for AI applications to access and utilize data securely. Major AI providers, like OpenAI and Google DeepMind, have adopted MCP, signaling its shift from a theoretical concept to a standard in enterprise infrastructure. The article explores several MCP patterns that help streamline the interaction between LLMs and various APIs and microservices. Key to MCP's effectiveness is its ability to address what the article terms the "N times M problem." This issue arises when numerous client applications need to interact with multiple servers and tools, leading to a tangled network of integrations. Solutions include resource consolidation, optimizing context for AI efficiency, and ensuring systems can adapt to evolving AI capabilities. Domain-Driven Design principles are highlighted as essential for maintaining separation between infrastructure and business logic, allowing for easier testing and adaptation to changes without extensive rewrites. The article emphasizes the importance of token-optimized response design. Unlike traditional APIs, each token in an MCP response affects the AI model's context window, so it’s critical to prioritize actionable information and minimize unnecessary details. It also suggests organizing tools into logical groups for better efficiency in handling user queries. By strategically positioning MCP servers as foundational enterprise infrastructure rather than isolated solutions, organizations can better prepare for the complexities of multi-model architectures and evolving AI technologies.