Describe What You Need. Get the SQL.
You know the data you want. “Show me all customers who ordered more than three times last month.” The problem is translating that into the right combination of JOINs, WHERE clauses, GROUP BY, and HAVING — and then remembering that PostgreSQL uses ILIKE while MySQL doesn’t, or that SQL Server wants TOP instead of LIMIT.
The SQL Query Generator takes your plain-English description, your SQL dialect choice, and an optional table schema, then produces a ready-to-use query. Paste in your schema and you’ll get your actual table and column names in the output. Skip the schema and it’ll use sensible defaults that you can rename.
This tool won’t replace knowing SQL. You’ll still need to review the generated query, check for edge cases, and make sure the logic matches your intent. But it dramatically speeds up the “blank editor” phase, especially for complex queries involving multiple joins or window functions where syntax errors eat up time.
What It Supports
- Five SQL dialects — PostgreSQL, MySQL, SQLite, SQL Server, and Oracle, each with their engine-specific syntax quirks
- JOINs, aggregations, subqueries, CTEs, and window functions
- Schema-aware generation when you paste your table definitions
- Natural language input — describe what you want, not how to get it
- Results in seconds
How to Use It
- Describe the query you need — be specific about what data, from which entities, and any filters or sorting
- Pick your SQL dialect
- Paste your table schema if you have it handy (this makes a big difference in output accuracy)
- Click “Generate SQL Query”
“Show me the top 10 products by revenue this quarter with their category names” produces a query with the right SELECT columns, a JOIN between products and categories, SUM aggregation on revenue, a WHERE clause for the current quarter, GROUP BY, ORDER BY revenue DESC, and LIMIT 10 (or TOP 10 for SQL Server).
Dialect Differences That Matter
PostgreSQL gets ILIKE for case-insensitive matching, array operations, and CTE syntax. MySQL uses backtick identifiers and its own date functions. SQLite handles dates differently than both. SQL Server uses square brackets and T-SQL conventions. Oracle has ROWNUM and its own function library. Picking the right dialect saves you from debugging syntax errors that have nothing to do with your logic.
Who Actually Uses This
- Data analysts writing ad-hoc queries for exploration and reporting — especially when the query gets complex enough that you’d normally need fifteen minutes just for the JOIN structure
- Developers building backend endpoints who need query scaffolds quickly
- People learning SQL who want to see how a plain-English request translates into actual syntax
- DBAs creating monitoring and migration queries where getting the structure right matters more than the specific values
- Non-technical team members who know what data they need but can’t write the SQL themselves
The Regex Generator does the same natural-language-to-code approach for regular expressions. The API Mock Generator creates realistic test data for development.
Honest FAQ
How important is providing a schema?
Very. Without one, the AI guesses table and column names. With one, it uses your exact structure. The difference between a query you can run immediately versus one you need to rewrite.
What about really complex queries?
It handles multi-table JOINs, nested subqueries, CTEs, and window functions. The more detailed your description, the better the output. Vague descriptions produce vague queries.
Should I run the generated SQL directly on production?
Please don’t. Review it first. Test it on a dev database. Check that the WHERE clause isn’t accidentally too broad. AI-generated queries can be logically correct but miss edge cases in your specific data.
What’s the cost?
Free, no accounts needed, use it as often as you want.