> It's for using lsp to do things like symbol renames. It's cheaper to call an LSP server to do those kind of simple refactors than to spend tokens on the model doing it.

Is OpenCode pushing the LLM into doing these refactors via LSP? I found that making these refactors with ast-grep is quite efficient.

> It can also use LSP to build context without having to grep around a ton. You don't have to grep and hten pull lines and make guesses for a function. You just ask the LSP for it.

I guess I was not particularly successful with having the agent actually do that. I think what fff is attempting to do (with fuzzy expansion of slightly inaccurate greps) results in better results to me, but even that is debatable.

To be fair, we do not have evals for this today, but we're generally looking into token efficiency and how likely models are at using tools the right way, and we did not find a ton of evidence of LSP helping, even for finding data.

But I would love to see some sessions from people where they have success with LSP data for either refactoring or looking up, because this would be super useful to better understand out blind spots.

To be fair in every harness I've ever used that has LSP support, they never actually utilize the LSP for more deterministic refactoring tools. And even then when I do enable the LSP in many harnesses oftentimes it doesn't even use it at all.

Maybe they haven't been taught to do so or it's not integrated into the system prompt or the tools but all of them only ever use the LSP to read files/symbols.

Every harness I've used will happily just call the edit tool over and over or do a find and replace via sed or programatically call a python/perl script rather than rename a symbol via other means.