SiteCheck SQL Generator

x402

SiteCheck SQL Generator is a paid API for AI agents from api.sitecheck-api.workers.dev, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-19).

Converts a plain-language question and a database schema into a syntactically correct SQL query for Postgres, MySQL, SQLite, or BigQuery

Price / call$0.003

Seller volume →—

Success—

StatusUnknown

How this service works

SQL from a question and a schema (CREATE TABLE statements or a JSON description of tables and columns), for postgres (default), mysql, sqlite or bigquery. The tables and columns used are checked against the schema; a query that does not match it is retried once, then refused. Writes (INSERT, UPDATE, DELETE, DDL) are refused unless allowWrite is true. The query is not run.

Output

Returns a JSON object containing the generated SQL string, a human-readable explanation of what the query does, the list of tables and columns referenced, the SQL dialect used, a readOnly boolean flag, a timestamp, and an advisory note reminding the caller to review the SQL before execution since it was not actually run.

Example use cases

Analytics query for order reporting

“I have a Postgres database with these tables: CREATE TABLE orders (id SERIAL, placed_at TIMESTAMP, status TEXT, total NUMERIC); CREATE TABLE customers (id SERIAL, email TEXT, country TEXT); — can you generate a SQL query that shows total revenue by country for orders with status 'completed' in the last 6 months?”

Read-only dashboard query generation

“Given this SQLite schema — CREATE TABLE sessions (id INTEGER PRIMARY KEY, user_id INTEGER, started_at TEXT, duration_seconds INTEGER); — write me a query that finds the average session duration per user for sessions longer than 60 seconds, and make sure it's read-only.”

BigQuery query from plain question

“My BigQuery table is defined as CREATE TABLE events (event_id STRING, user_id STRING, event_type STRING, occurred_at TIMESTAMP, properties JSON); — generate a BigQuery SQL query that counts unique users who triggered a 'purchase' event each day this year.”

Technical details

Endpoint

POSThttps://api.sitecheck-api.workers.dev/api/sql/generate?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to translate a plain-language data question into a syntactically correct SQL query against a known schema, particularly within an AI agent pipeline where writing SQL manually is impractical. It supports multiple dialects (Postgres, MySQL, SQLite, BigQuery), safely defaults to read-only queries, and returns column-level metadata useful for auditing. Prefer it over general-purpose LLM SQL generation when you want a pay-per-call, schema-grounded, structured response with explicit table and column references.

Side effects

Schema exceeds 20,000 character limit — request rejected
Question exceeds 1,000 character limit — request rejected
Ambiguous or underspecified question may produce incorrect SQL — review carefully
Unsupported dialect requested — defaults to postgres or errors
Schema syntax errors may cause malformed SQL output
allowWrite=false but write-type question asked — query may be refused or rewritten as read-only

Input

Schema · Request body

Field	Type	Description
schema	string	CREATE TABLE statements, or a JSON object of tables and column lists (max 20,000 characters)
dialect	string	postgres (default), mysql, sqlite or bigquery
question	string	Question in plain language (max 1,000 characters)
allowWrite	boolean	Allow INSERT, UPDATE, DELETE or DDL (default false)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "sql": "SELECT COUNT(id) FROM orders WHERE EXTRACT(YEAR FROM placed_at) = 2025",
    "note": "The query was not run. Tables and columns were checked against the schema you gave; review the SQL before running it.",
    "tables": [
      "orders"
    ],
    "columns": [
      {
        "table": "orders",
        "column": "id"
      },
      {
        "table": "orders",
        "column": "placed_at"
      }
    ],
    "dialect": "postgres",
    "readOnly": true,
    "checkedAt": "2026-10-15T18:21:17.416Z",
    "explanation": "This query counts the number of rows in the orders table where the year of the placed_at timestamp is 2025."
  }
}

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