让大模型直接调用金融数据接口，提升量化问题回答准确率
Bypassing Document Ingestion: An MCP Approach to Financial Q&A
- 大模型通过MCP协议直连LSEG数据接口，跳过文档检索
- 在财务数据集上多步计算题准确率达80.4%
- 适合量化分析场景，不适用于依赖文档细节的定性问题
回答金融问题通常被视为信息检索任务。但实际上，大量相关信息已存在于经过整理的供应商系统中，尤其是在量化分析领域。本文研究了在何种条件下，模型上下文协议（MCP）能否作为标准检索增强生成（RAG）的更可靠替代方案，使大语言模型（LLMs）直接与数据交互，而非依赖文档摄入和分块检索。我们构建了一个自定义MCP服务器，将LSEG API作为工具暴露，并在FinDER基准上进行评估。该方法在金融子集上表现优异，当相关上下文可被检索时，多步数值问题准确率最高达80.4%。论文既为基于MCP的金融问答提供了基准，也揭示了该方法的局限性——例如需要定性或文档特有上下文的问题。总体而言，直接访问结构化数据是量化金融问答的一种轻量高效替代方案，但无法取代所有金融问答任务。
原文摘要 · Abstract (English)
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.
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