The Model Context Protocol (MCP) offers a powerful way to connect Large Language Models (LLMs) with outside data sources, but using it effectively requires careful review of MCP servers and selection of the one that best meets integration requirements. To navigate this, it's essential to assess the security controls of MCP servers, including access control levels and schema enforcement, to prevent security incidents such as sensitive information being shared inadvertently. Additionally, evaluating how MCP servers manage integrations, including handling API providers' unique requirements and implementing authentication flows, is crucial to ensure reliable and accurate outputs. Furthermore, examining the tools provided by MCP servers, such as detailed names and descriptions, explicit parameter requirements, and robust error handling, can help determine their quality and effectiveness. By considering these factors, developers can make informed decisions when selecting an MCP server to support their product integrations and connect their LLM to hundreds of applications securely and reliably.