I think MCP's huge adoption was mainly due to its timing.

Tool calling was a thing before MCP, but the models weren't very good at it. MCP almost exactly coincided with the models getting good enough at tool calling for it to be interesting.

So yeah, I agree - most of the MCP excitement was people learning that LLMs can call tools to interact with other systems.

One thing about MCP that some people forget is that the models are post trained on MCP-based rollouts. I think people imagine that MCP was something people discovered about models but it’s deeper than that — models are explicitly trained to be able to interpret various and unseen kinds of MCP system prompts.

The exact same is true of these Claude Skills. Technically this is “just a system prompt and some tools”, but it’s actually about LLM labs intentionally encoding specific frameworks of action into the models.