Practical usage guide
What this tool does
JSON Schema Generator inspects one representative JSON value and creates a starter schema for the observed properties, primitive types, nested objects, and array items. It is useful when an existing API payload, event record, configuration file, or agent output example needs an explicit contract for documentation or validation.
Inference can describe only what appears in the sample. It cannot reliably know whether a missing field is optional, whether null is allowed, which numeric ranges are valid, or whether a string is an identifier rather than an email. Treat the result as an editable draft and compare it with more samples and the real business contract.
Quick start
Paste representative JSON
Use a complete object or array containing realistic nested values, nulls, empty collections, and boundary-looking fields rather than an artificially simple record.
Review inferred types and required fields
Check every property, especially values that may be absent, nullable, string-encoded numbers, mixed arrays, or different across records.
Test additional examples
Validate the draft against payloads with optional fields, empty arrays, unusual Unicode, boundary numbers, and error responses before enforcing it.
Common use cases
Bootstrap an API contract
Create an initial schema from an existing request or response before adding documentation, runtime validation, or contract tests.
Document events and configuration
Describe webhook events, queue messages, analytics records, or configuration files that previously existed only as examples.
Prepare structured agent output
Use a sample object as the starting point for a structured-output contract, then simplify it to the subset supported by the selected model provider.
Limitations and important notes
Optional and nullable fields are ambiguous
One example cannot reveal every legitimate absence or null value. Required lists and union types always need human review.
Empty or mixed arrays are difficult to infer
Empty arrays provide no item evidence, while heterogeneous arrays may create a broad anyOf schema that accepts more data than intended.
Business constraints are not inferred
Samples do not reliably reveal patterns, uniqueness, numeric ranges, cross-field rules, recursive references, permissions, or authorization requirements.