SQL Mocker

SQL Mocker accepts schema metadata — table names, column names, data types, primary keys, and relationships — and generates SQL from natural-language questions against a safe replica of that structure. The vendor states the tool does not request database credentials and never establishes a live connection; users run a metadata extraction script themselves inside their own database tool and paste or upload the results. From that point, the assistant handles generation, explanation, review, improvement, formatting, conversion, and troubleshooting against the replica, while dummy data generated from the schema lets you inspect query logic without exposing real rows.

The core differentiator is deliberate disconnection. Most SQL AI tools either request a live connection or generate queries from a prompt alone with no structural grounding. SQL Mocker sits between those two positions: it uses your schema and relationships to anchor generation, which reduces hallucinated column names and wrong joins, but it does so without storing credentials or touching production. Projects are saved locally in the browser, and the vendor offers a backup file option — no server-side storage of your schema metadata is described.

The tool fits developers and analysts who need to prototype SQL before a database access request clears, Power BI users who want to shape a dataset before connecting a report, and teams in regulated environments where direct AI-to-database connections are blocked by policy. It does not execute queries or validate results against live data, which means any logic that depends on actual row counts, distributions, or referential integrity in production will not surface as an error inside the tool — it surfaces later, in your database. The free tier caps queries at fifty, which is enough for a focused prototyping session but not for a sustained daily workflow without a paid subscription.