LLM Structured Output Validator

Agent Invocation

REST API

curl -X POST https://aidevhub.io/api/schema-validator/ -H "Content-Type: application/json" -d '{"schema": "{\"type\": \"object\"}", "target": "openai"}'

const res = await fetch("https://aidevhub.io/api/schema-validator//api/schema-validator/", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
  "schema": "{\"type\": \"object\", \"properties\": {\"q\": {\"type\": \"string\"}}}",
  "target": "openai"
}),
});
const data = await res.json();
console.log(data);

import requests

res = requests.post(
    "https://aidevhub.io/api/schema-validator//api/schema-validator/",
    json={"schema":"{\"type\": \"object\", \"properties\": {\"q\": {\"type\": \"string\"}}}","target":"openai"},
)
print(res.json())

{
  "valid": true,
  "errors": [],
  "warnings": [],
  "fixed_schema": null
}

How to Use LLM Structured Output Validator

1. 1

   Define JSON Schema

   Paste your JSON Schema or select from templates (object with properties, array of records, etc.). The tool validates the schema itself.

2. 2

   Paste LLM output

   Copy the structured response from your LLM (Claude, GPT, etc.) into the validation panel. Format can be JSON, YAML, or markdown code block.

3. 3

   Validate against schema

   Click 'Validate' to check if the output matches the schema. Shows specific field violations (type mismatch, missing required fields, enum mismatches).

4. 4

   Debug errors

   The tool highlights which fields failed validation and why. Suggests corrections for common issues like wrong data types or invalid enums.

5. 5

   Fix and retry

   Edit the JSON inline and re-validate. Once passing, export as TypeScript types or use in your application code.

Frequently Asked Questions