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

Sasan Mansouri, Edoardo Pilla, Mark Wahrenburg, Fabian Woebbeking

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 alternative to document-centric RAG for quantitative financial QA, but not a substitute for all financial QA tasks.

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

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 alternative to document-centric RAG for quantitative financial QA, but not a substitute for all financial QA tasks.
Paper Structure (14 sections, 10 figures, 2 tables)

This paper contains 14 sections, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Corporate financial analysis
  4. Financial QA datasets
  5. Retrieval-augmented generation
  6. Model Context Protocol
  7. Methodology
  8. System architecture
  9. Reference dataset
  10. Evaluation
  11. Results
  12. Conclusions
  13. Extended results
  14. Backend prompts

Figures (10)

  • Figure 1: LSEG APIs MCP server system architecture diagram.
  • Figure 2: FinDER average numeric density across answer and references by category. Carriage returns are removed from the Answer variable to avoid inflating the underlying number counts. Numeric density is defined at the granular level as the ratio between number and non-space substring occurrences.
  • Figure 3: FinDER average forward-looking word count across answer and references by category.
  • Figure 4: LSEG-powered LLM performance on FinDER by question category. Relative values computed over 990 and 4713 entries respectively for Financials and Other samples.
  • Figure 5: LSEG-powered LLM performance on FinDER by context quality and question category. An answer is defined as of high quality if the associated Context Relevance is equal or larger than 0.75. Relative values computed over 668 and 636 entries respectively for Financials and Other samples in the High-quality group, and over 322 and 4077 entries respectively for Financials and Other samples in the Low-quality group.
  • ...and 5 more figures