MCP, or Model Context Protocol, has gained traction in the last year, with many developers rushing to create MCP servers. Yet, many of these servers fail to deliver expected results, often because developers misinterpret MCP as a simple wrapper around REST APIs. The article argues that the issue lies not with the protocol itself but with how developers construct their servers. MCP should be viewed as a user interface for AI agents, which requires different design principles than those used for human developers. The article outlines six best practices for building effective MCP servers. First, focus on outcomes rather than operations. Instead of creating multiple tools for related tasks, combine them into a single, high-level tool to streamline agent interactions. Second, simplify argument structures. Use clear, top-level parameters instead of complex nested objects to reduce confusion for agents. Third, ensure that instructions and error messages provide context, helping agents understand how to use the tools effectively. Further, the article emphasizes the importance of curating tools. Limit the number of tools to 5-15 per server and design for discovery, allowing agents to quickly access the right tool. Naming conventions matter too; use descriptive, service-prefixed names for tools to avoid ambiguity. Lastly, when dealing with large datasets, implement pagination to manage context constraints efficiently. By following these guidelines, developers can create MCP servers that are more effective and user-friendly for AI agents.