Why would a coding agent ignore a retrieval interface that returns more precise results? I explored this question in a small study comparing lexical search with grep against LSP-backed semantic navigation. I expected semantic navigation to reduce noise and save tokens. Instead, agents often stayed with grep . When I forced them to use the semantic path first, task success sometimes fell. This is a question of LLM-friendliness. A tool is not friendly to a model merely because its results are precise. It must return enough context for the next step and present that context in an interface and output shape the model can use directly. Familiarity may also matter: the model may have learned similar action paths during training. The interface properties can be evaluated directly. Training support is a hypothesis consistent with these results, not something this study proves. The result is not a general argument against LSP.…