Call a location-only reference tool and you get file paths and line numbers. The agent cannot act on that, so it opens each file. Xu measured 15.2 follow-up reads per episode on a multi-file rename, with pass@1 at 0.67 [13]. Put the matching line and its two neighbours into the same response and reads fall to 3.2 while pass@1 rises to 0.83 [14]. Three lines of code per hit [1] bought 12 fewer reads per episode, a 79 percent cut [2]. The language server and its reference set were identical in both arms; only the reply text changed [19].
The caller-finding result reads like a clean win for LSP until the two figures are combined. Precision was 1.00 against grep's 0.76 [6], and recall sat near 0.66 for both [7]. Run 2PR/(P+R) and LSP scores about 0.80 against grep's 0.71, so perfect precision is worth roughly 0.09 F1 here [5]. Recall is the binding limit and both paths share it, because the agent stopped looking before it had every call [7].
Grep still won the rename outright at 1.00 pass@1, and the reason is the protocol's scope: a complete rename touches comments, documentation and strings that semantic reference tools exclude by design [15]. The padded LSP response closes 0.16 of that gap and leaves 0.17 [4]. A tool that answers correctly and unhelpfully is still a round trip.
The study does not report per-repository follow-up read counts, so the link between the token premium on remeda and requests [9][11] and the read amplification measured in the rename experiment is inference rather than measurement. It also offers no way to score lexical noise in advance. Xu's own framing is that semantic navigation earns its setup and interaction costs when a grep query returns many unrelated matches [12], which makes grep itself the cheapest diagnostic for whether you need something better than grep.
The forced-ordering arm is where the cost shows most plainly. Making the agent start with semantic navigation turned 11 of every 100 successful localization runs into failures [6], on tasks where it had picked semantic tools in at most 6 percent of free-choice runs anyway [3]. Asking for every reference, rather than mandating the tool, moved semantic use sharply [8].
Transfer conditions: three Claude models, two languages, a handful of named repositories, and a preliminary post dated August 12, 2026 [1][2]. Xu leads AgentConnect, an open-source platform that lets runtimes such as Claude Code and Codex keep their native tools and prompt-to-tool loops [17], and he reads the result as a technical argument for preserving those loops [18]. The per-repository token deltas are descriptions of those repositories. The response-format finding is the portable one, because everything except the string the tool returns was held fixed [19].
