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Schema detection tells the AI what your tables and columns are called. Custom context tells it what they mean. By annotating your schema with descriptions, you turn cryptic column names and undocumented conventions into knowledge the AI can act on — so it writes the query you intended, not the one your column names accidentally imply.

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The problem custom context solves

Real schemas are full of traps for an AI:
  • amount is stored in cents, not dollars.
  • status = 3 means “churned” — but nothing in the schema says so.
  • deleted_at IS NOT NULL marks soft-deleted rows that should usually be excluded.
  • mrr_v2 is the real revenue column; mrr is deprecated.
Without context, the AI guesses — and quietly returns wrong numbers. With context, it knows.

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How it works

In the dashboard, open a connection’s schema and add descriptions to:
  • Tables — what the table represents, and any rows that should typically be filtered out.
  • Columns — units, enumerations, conventions, and which columns are canonical vs. deprecated.
These descriptions are attached to the schema QueryBear returns from get_schema, so every AI client — Claude, Cursor, ChatGPT, and the rest — sees them when it plans a query.

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Example

Annotating the orders table:
“orders — one row per completed checkout. Exclude rows where status = 'void'. total_cents is in cents (divide by 100 for dollars). Use created_at (UTC) for time filters, not updated_at.”
Now when someone asks “what was revenue last month?”, the AI divides by 100, filters voids, and uses the right timestamp — without anyone re-explaining the schema every time.

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Why annotate once

Custom context lives with the connection, so the knowledge is shared across every client and every teammate. You document a quirk once, and every future query — by any agent, in any tool — benefits. It’s the difference between an AI that knows your data and one that merely sees it.
  • Schema detection — the structure custom context enriches
  • Memory — context QueryBear infers automatically over time