Bypassing Document Ingestion: An MCP Approach to Financial Q&A
This work addresses financial QA for quantitative analysis by providing a lightweight alternative to document-centric RAG, though it is incremental as it builds on existing MCP and RAG methods.
The paper tackled the problem of financial question answering by exploring whether Model Context Protocol (MCP) offers a more reliable alternative to retrieval-augmented generation (RAG) by allowing LLMs to interact directly with data, achieving up to 80.4% accuracy on multi-step numerical questions in the FinDER benchmark.
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 alternative to document-centric RAG for quantitative financial QA, but not a substitute for all financial QA tasks.