AI RESEARCH
Bypassing Document Ingestion: An MCP Approach to Financial Q&A
arXiv CS.AI
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ArXi:2603.20316v1 Announce Type: cross Answering financial questions is often treated as an information retrieval problem. In practice, however, much of the relevant information is already available in curated vendor systems, especially for quantitative analysis. We study whether, and under which conditions, Model Context Protocol (MCP) offers a reliable alternative to standard retrieval-augmented generation (RAG) by allowing large language models (LLMs) to interact directly with data rather than relying on document ingestion and chunk retrieval.