This paper demonstrates that Model Context Protocol (MCP) can outperform traditional RAG for quantitative financial Q&A by interacting directly with structured data APIs.
March 24, 2026
Original Paper
Bypassing Document Ingestion: An MCP Approach to Financial Q&A
arXiv · 2603.20316
The Takeaway
It challenges the 'ingest everything' document-centric RAG paradigm for finance. By using LLMs as tool-calling agents over curated vendor APIs, it achieves high accuracy on multi-step numerical questions that typically break standard chunk-based retrieval pipelines.
From the abstract
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 more 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 an