Most people think the biggest factor in AI performance is the model.

But often in enterprise settings, the architecture behind how internal data is connected matters even more.

A new benchmark from CData found a ~25% accuracy gap between different MCP server approaches.

Basically, two AI systems using the same model could return very different answers depending on how they access your data and what approach to MCP they use.

If you’re building AI agents, copilots, or internal tools that connect to CRM or project management systems… this is worth understanding.

You can check out the full benchmark, methodology & results from CData here: https://bit.ly/4s4o9p0

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Matt Wolfe

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.

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