The Aiera Lift: Measuring the Value of a Financial Data MCP Server Across Foundation Models
Frontier AI models are increasingly used for financial research, but they remain limited by what they learned during training and what they can find on the open web. This study measures what happens when leading foundation models are connected to Aiera’s permissioned financial data through a Model Context Protocol server. Using a private, source-grounded benchmark, the research quantifies the improvement in answer quality across models and reveals why access to better data is only part of the equation.
What You’ll Learn
- How 13 foundation models performed with and without access to Aiera’s financial data
- Why six models delivered significant improvement, producing answers that were approximately 2.4 times better on average
- How tool-use competence, rather than model size or capability tier, determines whether a model can retrieve and synthesize proprietary financial information
- Where the lift was most concentrated: gains were largest on the questions proprietary research uniquely answers
This paper provides a clear, data-backed look at how access to Aiera’s permissioned financial data improves AI-generated research answers across foundation models.