In the end it's hard to measure but personally I feel that my agent rarely misses any context for a given task, so I'm pretty happy with it.
I used a different approach than tree-sitter because I thought I found a nice way to get around having to write language-specific code. I basically use VSCode as a language backend and wrote some logic to basically rebuild the AST tree from VSCode's symbol data and other API.
That allows me to just install the correct language extension and thus enable support for that specific language. The extension has to provide symbol information which most do through LSP.
In the end it was way more effort than just using tree-sitter, however, and I'm thinking of doing a slow migration to that approach sooner or later.
Anyways, I created an extension that spins up an mcp server and provides several tools that basically replace the vanilla discovery tools in my workflow.
The approach is similar to yours, I have an overview tool which runs different centrality ranking metrics over the whole codebase to get the most important symbols and presents that as an architectural overview to the LLM.
Then I have a "get-symbol-context" tool which allows the AI to get all the information that the AST holds about a single symbol, including a parameter to include source code which completely replaces grepping and file reading for me.
The tool also specifies which other symbols call the one in question and which others it calls, respectively.
But yeah, sorry for this being already a quite long comment, if you want to give it a try, I published it on the VSCode marketplace a couple of days ago, and it's basically free right now, although I have to admit that I still want to try to earn a little bit of money with it at some point.
Right now, the daily usage limit is 2000 tool calls per day, which should be enough for anybody.
Would love to hear what you think :)
<https://marketplace.visualstudio.com/items?itemName=LuGoSoft...>