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sqlscan-check
x402
sqlscan-check is a paid API for AI agents from payai.agentstools.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15).
Pre-flight safety and performance analysis of a SQL query returning a pass/caution/block verdict with per-finding severity, reason, and fix suggestion.
Price / call$0.02
Success—
StatusUnknown
How this service works
Safety and performance preflight for a database query BEFORE you run it. Send the query for Postgres, MySQL or SQLite and get a pass, caution or block verdict with per-finding operation, severity, plain-language reason and a fix. Query-safety indicators, not a guarantee.
Output
A structured verdict (pass, caution, or block) for the submitted query, along with an array of findings each containing the SQL operation involved, a severity level, a plain-language explanation of the issue, and a recommended fix. Acts as a safety indicator rather than a guarantee of execution safety.
Example use cases
Preventing dangerous DELETE in production
“Check this SQL before I run it on our MySQL production DB — DELETE FROM users WHERE last_login < '2020-01-01' — I want to know if it'll block, what severity the risk is, and how to fix it if there's a problem.”
Detecting missing index on large table query
“Can you scan this Postgres query for performance issues? SELECT * FROM events WHERE user_id = 42 AND created_at > NOW() - INTERVAL '30 days' — the indexed columns are user_id and event_type, tell me if there's a full table scan risk.”
Vetting AI-generated SQL before execution
“My app just auto-generated this SQLite query from user input — can you check it for SQL injection or anything dangerous before I actually run it? Query: SELECT * FROM products WHERE category = 'shoes' AND price < 100”
Technical details
Endpoint
POSThttps://payai.agentstools.dev/sql/checkWhen to prefer
Use this endpoint when an AI agent or automated pipeline is about to execute a SQL query — especially user-supplied, AI-generated, or dynamically constructed queries — and you need a quick safety and performance gate before touching the database. Prefer this over generic linting tools when you need dialect-aware (Postgres/MySQL/SQLite/BigQuery/Snowflake) analysis with actionable per-finding fixes. Ideal for agent workflows that construct SQL from natural language and need a guardrail layer before execution.
Side effects
- Unsupported dialect submitted — only postgres, mysql, sqlite, bigquery, snowflake are accepted
- Malformed or unparseable SQL returns an error or inconclusive verdict
- False negatives: complex nested queries may not catch all issues
- Missing indexed_columns hint may produce less accurate performance findings
- Empty or blank sql field returns a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| sql | string | The query text to check |
| dialect | string | Database dialect (default postgres) |
| options | object | Optional hints, e.g. indexed_columns: list of column names to refine performance findings |
Tags
x402
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