I agree the current way tools are used seems inefficient. However there are some very good reasons they tend to operate on code instead of syntax trees:
* Way way way more code in the training set.
* Code is almost always a more concise representation.
There has been work in the past training graph neural networks or transformers that get AST edge information. It seems like some sort of breakthrough (and tons of $) would be needed for those approaches to have any chance of surpassing leading LLMs.
Experimentally having agents use ast-grep seems to work pretty well. So, still representing a everything as code, but using a syntax aware search replace tool.
* Way way way more code in the training set.
* Code is almost always a more concise representation.
There has been work in the past training graph neural networks or transformers that get AST edge information. It seems like some sort of breakthrough (and tons of $) would be needed for those approaches to have any chance of surpassing leading LLMs.
Experimentally having agents use ast-grep seems to work pretty well. So, still representing a everything as code, but using a syntax aware search replace tool.