Comment by simonw

I'm very excited about tool use for LLMs at the moment.

The trick isn't new - I first encountered it with the ReAcT paper two years ago - https://til.simonwillison.net/llms/python-react-pattern - and it's since been used for ChatGPT plugins, and recently for MCP, and all of the models have been trained with tool use / function calls in mind.

What's interesting today is how GOOD the models have got at it. o3/o4-mini's amazing search performance is all down to tool calling. Even Qwen3 4B (2.6GB from Ollama, runs happily on my Mac) can do tool calling reasonably well now.

I gave a workshop at PyCon US yesterday about building software on top of LLMs - https://simonwillison.net/2025/May/15/building-on-llms/ - and used that as an excuse to finally add tool usage to an alpha version of my LLM command-line tool. Here's the section of the workshop that covered that:

https://building-with-llms-pycon-2025.readthedocs.io/en/late...

My LLM package can now reliably count the Rs in strawberry as a shell one-liner:

  llm --functions '
  def count_char_in_string(char: str, string: str) -> int:
      """Count the number of times a character appears in a string."""
      return string.lower().count(char.lower())
  ' 'Count the number of Rs in the word strawberry' --td