SQL Generator

Write SQL from a plain-language request. Give it your table schema for best results. Supports PostgreSQL, MySQL, SQLite and SQL Server.

Pay by wallet (x402) AI-generated sql-generate

Example

Example answer

{
  "sql": "SELECT * FROM users WHERE active = true;",
  "dialect": "postgres",
  "joins_used": [],
  "explanation": "This query retrieves all active users from the users table, selecting all columns (id, name, email, active, created_at) for users where the active flag is set to true.",
  "tables_referenced": [
    "users"
  ],
  "estimated_complexity": "simple"
}

Inputs

Field	Type	Required	What it is
`dialect`	string	No	postgres/mysql/sqlite/mssql
`table_schema`	string	No	—
`natural_language_query`	string	Yes	—

Output fields

How reliably each field appears in a successful answer.

Field	Type	Appears
`sql`	string	Always (present in every successful answer)
`explanation`	string	Always (present in every successful answer)
`tables_referenced`	array	Always (present in every successful answer)
`estimated_complexity`	string	Always (present in every successful answer)

How to call it

The same tool through each route. MCP clients reach it through Strale’s server at https://api.strale.io/mcp; the call below is what the agent sends.

# Needs an API key. max_price_cents caps what this call may cost.
curl -X POST https://api.strale.io/v1/do \
  -H "Authorization: Bearer $STRALE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "capability_slug": "sql-generate",
    "inputs": {
      "dialect": "postgres",
      "table_schema": "users(id INT, name TEXT, email TEXT, active BOOL, created_at TIMESTAMP)",
      "natural_language_query": "en"
    },
    "max_price_cents": 5
  }'

{
  "tool": "strale_execute",
  "arguments": {
    "slug": "sql-generate",
    "inputs": {
      "dialect": "postgres",
      "table_schema": "users(id INT, name TEXT, email TEXT, active BOOL, created_at TIMESTAMP)",
      "natural_language_query": "en"
    },
    "max_price_cents": 5
  }
}

import { Strale } from "straleio";

const strale = new Strale({ apiKey: process.env.STRALE_API_KEY! });
const result = await strale.do({
  capability_slug: "sql-generate",
  inputs: {
    "dialect": "postgres",
    "table_schema": "users(id INT, name TEXT, email TEXT, active BOOL, created_at TIMESTAMP)",
    "natural_language_query": "en"
  },
  max_price_cents: 5,
});

import os
from straleio import Strale, DoRequest

strale = Strale(api_key=os.environ["STRALE_API_KEY"])
result = strale.do(DoRequest(
    capability_slug="sql-generate",
    inputs={
        "dialect": "postgres",
        "table_schema": "users(id INT, name TEXT, email TEXT, active BOOL, created_at TIMESTAMP)",
        "natural_language_query": "en",
    },
    max_price_cents=5,
))

# No account. The first request answers 402 with the price in USDC;
# an x402 client pays it from your wallet and repeats the request.
curl -i 'https://api.strale.io/x402/v2/sql-generate?dialect=postgres&table_schema=users%28id+INT%2C+name+TEXT%2C+email+TEXT%2C+active+BOOL%2C+created_at+TIMESTAMP%29&natural_language_query=en'

Limitations

Complex queries need testing against your schema
AI-generated SQL — complex joins and subqueries should be tested against your schema before running on production
What to do instead: Test generated queries against a staging database with representative data volume to verify correctness and performance