For decades, we have adapted to software. We learned shell commands, memorized HTTP method names and wired together SDKs. Each interface assumed we would speak its language. In the 1980s, we typed 'grep', 'ssh' and 'ls' into a shell; by the mid-2000s, we were invoking REST endpoints like GET /users; by the 2010s, we imported SDKs (client.orders.list()) so we didn’t have to think about HTTP. But underlying each of those steps was the same premise: Expose capabilities in a structured form so others can invoke them.But now we are entering the next interface paradigm. Modern LLMs are challenging the notion that a user must choose a function or remember a method signature. Instead of “Which API do I call?” the question becomes: “What outcome am I trying to achieve?” In other words, the interface is shifting from code → to language. In this shift, Model Context Protocol (MCP) emerges as the abstraction that allows models to interpret human intent, discover capabilities and execute workflows, effectively exposing software functions not as programmers know them, but as natural-language requests.MCP is not a hype-term; multiple independent studies identify the architectural shift required for “LLM-consumable” tool invocation. One blog by Akamai engineers describes the transition from traditional APIs to “language-driven integrations” for LLMs. Another academic paper on “AI agentic workflows and enterprise APIs” talks about how enterprise API architecture must evolve to support goal-oriented agents rather than human-driven calls. In short: We are no longer merely designing APIs for code; we are designing capabilities...