Top-K Embedding Retrieval Answers 15.7% of Financial-Report Questions; An Agent With grep-Style MCP Tools Answers 58.8%
READ (2608.06305, submitted 2026-08-06) argues chunk-and-embed RAG is structurally unsound for tables: on a 780-page government financial report, 86.8% of content lines are table rows, and a figure inherits its unit from a header a median of 13 lines above it, so a chunk boundary routinely separates a number from whether it is in lakh or crore — a two-order-of-magnitude error. Replacing the vector index with three deterministic operations exposed over MCP (normalized lexical search, structural navigation, bounded span reads) took accuracy on 51 verified questions from 15.7% to 58.8% (p_Holm = 2e-5), and still led a tuned dense baseline by 23.5 points. The control matters most for builders: the same agent loop given a top-k tool instead reached only 27.5%, locating the gain in the retrieval interface rather than in agentic iteration.
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