The Model Context Protocol (MCP) is explored as a means for AI applications to extend functionality through a client-server plugin system, likened to a USB-C port for AI. Justin Spahr-Summers and David Soria-Para from Anthropic, the co-creators of MCP, detail its origin as an internal developer tool to integrate Anthropic's models more deeply, born from frustrations of copying data between isolated tools. MCP draws inspiration from the Language Server Protocol (LSP), addressing the "M times N" integration problem, but innovates with primitives like tools, resources, and prompts to offer application developers more control over user experience. The discussion covers the design considerations behind these primitives, advocating for a shift from tool-calling everything to leveraging resources and prompts for richer AI interactions. They also touch on the future of MCP, including composability, state management, and the balance between open standards and practical implementation.
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