[2603.20316] Bypassing Document Ingestion: An MCP Approach to Financial Q&A

Computer Science > Information Retrieval arXiv:2603.20316 (cs) [Submitted on 19 Mar 2026] Title:Bypassing Document Ingestion: An MCP Approach to Financial Q&A Authors:Sasan Mansouri, Edoardo Pilla, Mark Wahrenburg, Fabian Woebbeking View a PDF of the paper titled Bypassing Document Ingestion: An MCP Approach to Financial Q&A, by Sasan Mansouri and 3 other authors View PDF HTML (experimental) 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 and chunk retrieval. We test this by building a custom MCP server that exposes LSEG APIs as tools and evaluating it on the FinDER benchmark. The approach performs particularly well on the Financials subset, achieving up to 80.4% accuracy on multi-step numerical questions when relevant context is retrieved. The paper thus provides both a baseline for MCP-based financial question answering (QA) and evidence on where this approach breaks down, such as for questions requiring qualitative or document-specific context. Overall, direct access to curated data is a lightweight and effective altern...