Why context engineering belongs in the data warehouse

AI agents often struggle not because of model choice but because they lack well-managed business context, which must be supplied within limited and costly context windows. While Model Context Protocol (MCP) offers a convenient way to connect agents directly to many live data sources, large numbers of tools can consume substantial tokens, reduce tool-selection accuracy, trigger source API limits, duplicate requests, and expose sensitive data through source-system credentials. The proposed alternative is to ingest and preprocess structured and unstructured business data in a data warehouse, using tools such as Fivetran for synchronization and dbt for SQL-based modeling, then provide agents with compact, transformed, governed context rather than raw data on demand. Warehouse-based context engineering can reduce token use and latency, enable data testing, documentation, lineage, and read-oriented access controls, with dbt’s context-engineering package supporting functions such as chunking, embeddings, classification, and extraction. MCP remains useful for rapidly changing information, early experimentation, and natural-language access to warehouse data, while organizations are encouraged to identify costly MCP sources and model their highest-value context in the warehouse.

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