The ToolSense framework exposes a gap between retrieval performance and actual understanding in LLM agents. Researchers found that standard benchmarks overstate capabilities by using constrained decoding and verbose queries. This diagnostic tool forces models to prove parametric knowledge without shortcuts. Developers can now better identify when a model truly understands a tool's semantics.