How AI Gets Data Wrong (and how to fix it)

A benchmark report from Catata reveals a ~25 percentage point accuracy gap between different MCP (Model Context Protocol) server architectures when connecting AI models to data sources. Systems that translate prompts directly into API calls struggle with complex queries, misinterpreting filter logic or pulling from wrong tables. Catata's approach, using a standardized relational interface with semantic context, achieved 98.5% accuracy versus 65–75% for other MCP implementations. The key takeaway is that the architecture between the AI model and the data — not the model itself — determines output accuracy.