Understanding MCP features: Tools, Resources, Prompts, Sampling, Roots, and Elicitation

Model Context Protocol (MCP) is emerging as a critical framework for enabling large language models (LLMs) to interact with external tools and data, offering features such as Tools, Resources, Prompts, Sampling, Roots, and Elicitation. MCP servers expose capabilities that allow clients to perform actions, access data, and guide models through reusable instructions, while tools require explicit user approval for execution, ensuring transparency and control. Resources provide read-only data for browsing without triggering operations, and prompts offer standardized templates for consistent task execution. On the client side, Sampling allows AI models to perform tasks on behalf of servers, Roots define secure filesystem boundaries, and Elicitation enables models to request additional user input during sessions, ensuring accurate responses. Together, these server and client features enable seamless, coordinated workflows that maintain privacy and user control, positioning MCP as a foundation for scalable and trustworthy AI applications.