JSON to Zod Schema Generator

The generator produces z.string(), z.number() (with .int() refinement for integer values), z.boolean(), z.null(), z.array(), and z.object() with fully nested schemas. When a value cannot be reliably typed, it falls back to z.unknown(). The type inference examines actual values in your JSON, so a field containing 42 becomes z.number().int() while 3.14 becomes z.number().

Yes, by default the generator adds a type alias using z.infer<typeof schema> so you get both runtime validation and compile-time TypeScript types from a single source of truth. This means you never need to manually write a separate interface — the Zod schema IS your type definition. You can toggle this off if you only need the validation schema.

The generator infers the array element type from the first item in the array. For mixed-type arrays where elements have different shapes, the first element's type is used as the schema. If you have heterogeneous arrays (e.g., [1, "hello", true]), you will need to manually adjust the output to use z.union([z.number(), z.string(), z.boolean()]). For arrays of objects with slightly different shapes, consider using z.object() with optional fields.

Yes. The output is valid TypeScript that imports from "zod". Just install zod (npm install zod), paste the generated code into a .ts file, and use it immediately for validation.

JSON Schema is a language-agnostic specification for describing JSON structure, used in OpenAPI/Swagger and many validators. Zod is TypeScript-specific and provides runtime validation plus compile-time type inference in one package. If you need cross-language validation, use JSON Schema. If you are in a TypeScript-only project and want the best developer experience, Zod is the better choice.

Yes. After generation, you can chain Zod refinements like .min(), .max(), .email(), .url(), .regex(), and custom .refine() validators onto any field. The generated schema is a starting point — add business-logic validation as needed for your use case.

JSON null values produce z.nullable(z.string()) (or the appropriate type). Fields that are completely absent in some objects would need z.optional(). Since JSON doesn't distinguish between "missing" and "null", you may need to adjust between z.optional(), z.nullable(), and z.nullish() based on your API's behavior.

z.infer<typeof mySchema> extracts the TypeScript type from a Zod schema, giving you a static type that exactly matches your runtime validator. This means you define your data shape once (the Zod schema) and get both validation and types — eliminating the risk of your types drifting out of sync with your validation logic.

Yes. A common pattern is to generate a Zod schema from your expected environment variable shape, then validate process.env at application startup. Libraries like @t3-oss/env-nextjs and znv use this exact pattern. Just adjust the generated schema to use z.string() for all env vars (they are always strings) and add .transform(Number) for numeric values.