Lists every table in the e-commerce database with a plain-language explanation of what it holds, its row count, and its column names. Also returns the relationships between tables and the convention this database uses for revenue. Use this first when you need to know what data exists, or to answer questions about the database's structure. Reads SQLite directly: no LLM call, no API key, no cost, returns immediately. Returns JSON.

Returns everything about one table: its purpose, every column with type, nullability, primary key and a plain-language description, its foreign keys, its CREATE TABLE statement, caveats worth knowing before querying it, and — for date columns — the range the data actually covers, so you can tell an empty result from an out-of-range question. Use it before writing SQL against a table you have not queried yet. Reads SQLite directly: no LLM call, no API key. Returns JSON.

Runs a single read-only SQL query against the e-commerce SQLite database and returns structured rows plus column names as JSON. Use this to answer analytical questions yourself — joins, aggregates, multi-step work — and when you need the actual values rather than a written summary. Call list_tables and describe_table first if you do not know the schema.

LIMITATIONS: SELECT only. The statement must be a single SELECT (or WITH ... SELECT, or VALUES); anything that writes data, changes schema, or alters connection state is rejected, as is more than one statement per call. SQLite dialect. Results are capped at 100 rows per call — use offset to page through more, and check hasMore in the response. No LLM is involved: no API key needed, no cost, and the result is exact.

Revenue convention: count every order whose status is not 'cancelled' (i.e. new, processing, shipped and completed all count as revenue). Cancelled orders are excluded because the sale did not complete. To count only fully delivered sales instead, filter status = 'completed' and say so in the answer.

Answers questions about the online shop in plain language: customers, products, stock, orders and sales. Ask it the way a shop owner would ask a colleague — no SQL, no table names, no technical wording needed.

USE THIS for any question about the shop or the business behind it, however casually it is phrased. For example: 'what sells best?', 'how many customers do we have?', 'which products are nearly out of stock?', 'how much did we earn last month?', 'who are our biggest spenders?', 'are any orders stuck in processing?', 'what was in order 42?', 'which category makes the most money?', 'how many orders were cancelled?'.

IT KNOWS ABOUT: customers (names, email, phone, when they signed up), products (name, category, price, stock on hand), orders (date, status — new, processing, shipped, completed, cancelled — and total), and the individual products inside each order (quantity and price paid).

RETURNS: a written answer in everyday language, with Markdown tables when a list or comparison helps. Numbers are read out of the text rather than returned as structured data.

GOOD TO KNOW: it can only look things up — it can never add, change or delete anything, and any request to do so is refused. One answer covers at most 100 rows of data. It asks a language model to write the SQL behind the scenes, so it needs an API key (or a local Ollama) configured, takes a few seconds, and may phrase the same question slightly differently between runs.