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FastX402 SQL Table Schema Parser
x402
FastX402 SQL Table Schema Parser is a paid API for AI agents from fastx402.tools, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-29).
Parses a SQL CREATE TABLE statement and returns a structured breakdown of the table name, column names, types, and constraints
Price / call$0.002
Success—
StatusUnknown
How this service works
200 pay-per-call tools for AI agents, from $0.001 per request in USDC on Base or Solana with x402. No account, no API key.
Output
A JSON object containing the table name as a string and a columns array where each entry includes the column name, data type, and an array of constraint strings (e.g. PRIMARY KEY, NOT NULL, UNIQUE, DEFAULT values).
Example use cases
Auto-document database schema
“I have this SQL DDL and I need to understand all the columns, their data types, and constraints for documentation purposes — can you parse this CREATE TABLE statement: CREATE TABLE products (id INTEGER PRIMARY KEY, sku TEXT NOT NULL UNIQUE, price DECIMAL(10,2) NOT NULL, stock INTEGER DEFAULT 0);”
Validate schema before migration
“Before I run this migration, can you break down the structure of this CREATE TABLE statement so I can verify the columns and constraints look right? CREATE TABLE sessions (session_id UUID PRIMARY KEY, user_id INTEGER NOT NULL, expires_at TIMESTAMP NOT NULL, data TEXT);”
Generate ORM model from SQL
“I want to build an ORM model from this raw SQL — parse the column names, types, and constraints from this CREATE TABLE so I can map them to class fields: CREATE TABLE invoices (id SERIAL PRIMARY KEY, client_name TEXT NOT NULL, amount NUMERIC NOT NULL, due_date DATE, paid BOOLEAN DEFAULT FALSE);”
Technical details
Endpoint
GEThttps://fastx402.tools/sql-table-schema?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, no-setup way to programmatically extract structured schema metadata from a SQL CREATE TABLE statement — especially in automated pipelines, code generation tools, or documentation workflows where parsing DDL manually is error-prone. It is ideal for agents that need column-level metadata (names, types, constraints) without running a live database connection.
Side effects
- Invalid or malformed SQL syntax returns an error or empty result
- Non-CREATE TABLE statements (e.g. SELECT, INSERT) may not be supported
- Very complex or dialect-specific SQL extensions may not parse correctly
- Missing required 'sql' query parameter returns a 400-style error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"columns": [
{
"name": "id",
"type": "INTEGER",
"constraints": [
"PRIMARY KEY"
]
},
{
"name": "name",
"type": "TEXT",
"constraints": [
"NOT NULL"
]
},
{
"name": "email",
"type": "TEXT",
"constraints": [
"UNIQUE"
]
},
{
"name": "created_at",
"type": "TIMESTAMP",
"constraints": [
"DEFAULT CURRENT_TIMESTAMP"
]
}
],
"table_name": "users"
}
}Tags
x402
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