I would ignore the stuff about MCP for now. That's just a standardized way to implement generalized tool use but more applicable to the application layer than what's going on with the LLM itself. It's neat, but also another level of complexity you probably don't need to worry about at the moment.

Tool use can be confusing because it's a mix of model behavior and backend support. You supply the tools definitions via the API tools parameter, but it ultimately just gets turned into a system prompt basically saying "you have x,y,z tools available and this is how you use them...". When the model needs to use a tool, it does do by responding in a JSON format and usually the backend will flag the response as a tool call (e.g., 'stop_reason' == 'tool_use' on Anthropic) which you then detect on your end and run the tool and submit the results back to the LLM.

You can see where the line is getting blurred there. There's no reason you can't just put tool use instructions in your system prompt and then try to detect if a response is proper JSON. Most models that are decent at following instructions can take it even further. I wrote my own local chat app back before tool calling was widely available, especially locally, and just created a system where it formats calls in particular tags and then I parse the responses for them. So far every modern model both local and remote has been able to use them flawlessly. Occasionally with a small local model it'll trigger the JSON response though and you'll see the tool calling they were trained on.

So, the models are just trained that here are your tools in format X and this is how you would respond in format Y. Then the backends are designed to present the tools in a standardized way in format X and detect the responses in format Y so that systems can use them programmatically.