IntelliSense

✓ functional

Completions that pay attention to your query, schema, and, when useful, the data itself.

Good completion should feel like a quiet assist, not a slot machine. SQLly builds suggestions from one SQL model engine: a shared view of your schema and data, rather than a pile of disconnected regexes. It can therefore see where the cursor is in the statement and offer the things that belong there.

Scope-aware

SQLly can tell whether you are in a FROM, WHERE, JOIN ... ON, or EXEC parameter list, and adjusts accordingly.

Schema-qualified

Two-part and schema-qualified names complete cleanly, including the system catalogs SQLly discovers.

Procedure-aware

EXEC parameters and stored-procedure filtering use the same model, with value metadata when it is available.

Careful auto-triggering

Suggestions appear when they help and stay quiet when they do not; the trigger matcher is checked against regressions.

The two ideas below - schema awareness and data awareness - are what let completion work from the database in front of you rather than a generic SQL dictionary. The same model keeps reading after the query runs: column provenance is how a result column knows which table and column it came from.

In this section

Schema — Suggestions grounded in the tables, columns, routines, and relationships you actually have.
Data awareness — When a column's values matter, SQLly can use them to make the next suggestion more useful.
Column provenance — SQLly remembers which table and column each result column really came from, so tools can act on the source instead of the label.