A lot of tools like Claude or ChatGPT have internal tools they call when they do math (or use a python script) rather than have the model actually compute anything.

The underlying tech itself can’t do it because you can’t do math by token probability.

That's not lying. There's nothing linguistic about numerical computation.

All of these features are not something the models themselves can do, but are grafted on.

I could easily write a Home Assistant automation pattern matching for nearly every way someone could say "how many Rs are in strawberry", depluralize a plural letter, and run it against "wc" in a bash terminal.

That doesn't mean it's smarter. It's that I've added something specific to it.

MCP and the like is just that too, gluing on functions or the ability to hopefully invoke a function. That's why so many hilariously mundane ones exist.

At the core, it's still a large language model: a statistical model of frequency of word and word chunk (token) patterns.

Sometimes one model can invoke another via that tooling but it's still a grafting on. It isn't a singular thing or system, but disjointed pieces so completely detached from how brains work.

This isn't AI hate, it's reality. I love the field of artificial intelligence and machine learning. It's cool as hell. But an LLM is fundamentally incapable of being anything more than an LLM with glued on pieces that invoke functionality.

OpenAI saw people mock the inability to count so they wrote a specialized tool to count letters and glued it on.

The world is full of endless edge cases. The inability to simply resolve them without gluing on every single one means it just isn't doing anything new.