Run AI Locally on Your Mac with Ollama: Chat, Tools, RAG and a Custom Model (Real Numbers from a Mac mini M4)

Good catch, thank you! You're right: I shortened the loop for the article, and that version always calls get_disk_free() and re-appends the assistant message for every call. The code in the repo does it your way: a TOOLS dict mapping each name to its function, response.message appended once, then TOOLS[call.function.name](**call.function.arguments) for each call. It doesn't validate arguments yet, so I'll add that and fix the snippet in the article. The full version is tools.py in the GitHub repo linked at the top of the post.

Thanks for separating the article snippet from the repository implementation. My criticism applies to the displayed loop, and the mapping plus single assistant-message append in the full version already addresses those two issues.

For argument validation, I would reject unknown tool names and unexpected fields before invoking a handler, then return a structured tool error tied to that call. A regression with two different calls in one response would check dispatch, result ordering and conversation history together, while a malformed argument case checks that neither handler runs accidentally.