DataDoctor

Runs entirely in your browser — no uploads, no saved snippets, no public links.

Paste a file, inspect the issues, export reviewed output.

Schema generation

JSON Schema Generator

Infer a starter JSON Schema from representative JSON data before refining required fields and constraints.

Paste representative JSON or load the broken sample to test validation.

Next workflow

Continue the preflight

JSON Schema Validator

Validate data against the generated schema.

Schema Checker

Check JSON or CSV data against a schema.

JSON Validator

Validate sample JSON before generating a schema.

After the tool runs

JSON Schema Generator review guide

Use the tool above first. The supporting notes below help you interpret the result, fix the right issues in the right order, and choose the next DataDoctor tool without pushing SEO content above the actual task.

Best input

creating a starter JSON Schema from sample data before refining required fields and constraints.

Output to keep

Keep the schema, sample data and validation report together so contract changes are traceable.

Next check

Refine required fields, enums and type rules, then validate a second sample before relying on the contract.

What it checks

JSON Schema Generator for real data work

JSON Schema Generator is for contract work: it checks whether the data shape matches the rules another system expects. Use it after the raw JSON or CSV parses cleanly.

  • Root data type
  • Object properties
  • Array item shapes
  • Inferred field types

Fix these first

Common errors to review before downstream work

Most failures come from small file issues that become expensive only after an API call, import job or spreadsheet cleanup. Fix blocking errors first, then re-run the same tool before moving forward.

  • Treating one sample as a final contract
  • Missing optional examples
  • Mixed item shapes
  • Required fields inferred from incomplete data

Recommended workflow

Run the check in this order

When the report points to a path or row, fix the data when the source is wrong and fix the schema only when the contract was written too narrowly.

  1. Step 1

    Paste representative JSON

  2. Step 2

    Generate the starter schema

  3. Step 3

    Review inferred types and required fields

  4. Step 4

    Validate future payloads against the refined schema

How to interpret a passing result

A pass means this specific preflight did not find the issues listed above. It is not a guarantee that the target system will accept every row, field, custom mapping or account-specific rule.

A generated or passing schema is not a final data contract until optional fields, nullable values and edge cases are reviewed.