JSON Schema Generator (Infer from Sample)

How to use

1. Paste a representative sample of your JSON data.
2. Click Run to generate the inferred schema.
3. Review and tighten the schema, inference can only see what your sample shows.

JSON Schema is the standard vocabulary for describing the shape of JSON data: which properties exist, their types, which are required, and what constraints apply. It powers request validation in API frameworks, form generation, editor autocomplete for config files, and, increasingly, structured output enforcement for large language models, where the schema tells the model exactly what shape of JSON to produce.

Writing a schema by hand for an existing payload is tedious and error-prone, which is what inference solves: give it one representative document and it produces a schema with correct types for every field, nested object and array structures mapped, and all observed properties listed.

Understand inference's fundamental limit: it describes your sample, not your domain. If your sample happens to have an optional field present, the schema will consider it required. If a field is null in the sample, the inferred type is null rather than the string it usually holds. If an array is empty, its item type is unknown. Treat the output as a first draft, feed the most complete sample you have, then relax or tighten constraints by hand.

Inferred schemas are also excellent documentation seeds: a schema plus one example is often clearer API documentation than paragraphs of prose.

Examples

Input:  {"name": "Asha", "age": 29, "active": true}
Output: {"type": "object", "properties": {"name": {"type": "string"}, "age": {"type": "integer"}, "active": {"type": "boolean"}}, "required": ["name", "age", "active"]}

Input:  {"items": [{"id": 1}, {"id": 2}]}
Output: {"type": "object", "properties": {"items": {"type": "array", "items": {"type": "object", "properties": {"id": {"type": "integer"}}}}}}

Frequently asked questions

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