Comment by ActorNightly

Everyone is sort of missing the point here.

While the title is quite obnoxious, the author is right.

I don't think that anyone would argue against standardizing training for any model on ways of invoking tools through specific output templates (with MCP being an extension of that). However, the question is what is the best way of having the model use those tools? There are 2 options

1 - Encode actual functionality during training, let the model figure out how to use available tools to do what it needs to. Latest Claude models are a good example of this, when editing files if it encounters issues with the under the hood tool, it will write a bash python command to edit the file

2 - Describe functionality in instruction context. This allows you to define complex sequences of things to do, but at the risk of the model losing context as the conversation continues.

3 - Use tool calling, where every request gets an available tools section appended to it, and define the complex functionality in the static code (whether its local tools or MCP servers)

Ideally, if we are pushing towards smarter models, the answer is between 1 and 2, where you have a model that only has access to be able to run shell commands, and some memory that it can reference on sequences of shell commands to run. An MCP invocation is then a simple echo jsonrpc pipe to local executable or a curl command. Eventually, its probably worthwhile to have your LLM run in a CPU like sandbox where it can execute arbitrary assembly commands from sequences stored in memory to do what it needs to do.

Until then, 2 and 3 are really what we have for adapting with current frameworks.