If I understand your point correctly - the main bottleneck for tool-calling/MCP is the models themselves being relatively terrible at tool-calling anything but the tools they were finetuned to work with until recently. Even with the latest developments, any given MCP server has a variable chance of success just due to the nature of LLM's only learning the most common downstream tasks. Further, LLM's _still_ struggle when you give them too many tools to call. They're poor at assessing the correct tool to use when given tools with overlapping functionality or similar function name/args.

This is what people mean when they say that MCP should maybe wait for a better LLM before going all-in on this design.