Author here, coding agents on C/C++ repos still have <10% acceptance on PRs. This problem is amplified on private repos which are not in models' training data. Anthropic released a clangd plugin for this. These experiments included vanilla claude code, vs with the plugin + pre-built index + instructions to use it on 255 issues from 40 C/C++ repos, including the bugs fixed after the model’s training cut off date. Couple of interesting findings:

The model prefers to use grep disproportionately. When LSP is optional, agent called it in 5% of runs

When required, 89% calls were plain name lookups and still 70% of the runs used grep

Fixes are close to flat (270 vs 275 out of 359 repair attempts) and cost went up 11%.

From the issue text alone, with no repo, the model already knows every fix site for 126 out of 255 issues.

Code and sample tasks: https://github.com/ByteAsk/lsp-claude-code-cpp

A caveat here is the setup tests this workflow and instructions. Has anyone seen LSP clearly help an agent on large codebases work on end to end tasks?

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