Ready to generate
Paste JSON and click Generate Schema// convert json samples into zod schemas for typescript
Convert JSON samples into Zod validation schemas for TypeScript. Generate z.object(), z.array(), z.string(), z.number() and nested types instantly.
Ready to generate
Paste JSON and click Generate SchemaPaste any JSON object or array into the input panel on the left.
Choose schema name, toggle nullable/optional handling and whether to include import + infer.
Click Generate Schema, then Copy All to paste straight into your TypeScript project.
Zod is a TypeScript-first schema declaration and validation library. This tool takes any JSON sample and generates the corresponding Zod schema, including nested objects, arrays, nullable values, and a z.infer<> type alias — saving you from writing boilerplate by hand.
Zod is a TypeScript-first schema validation library that lets you declare the shape and constraints of your data once, then automatically infers the TypeScript type. It replaces manual type assertions and makes runtime validation type-safe, which is especially valuable when consuming external APIs or user input.
Yes. The generator recursively traverses the JSON structure. Nested objects become nested z.object() calls, arrays become z.array() wrapping the inferred element type, and arrays of objects generate a fully typed z.array(z.object({...})) schema.
When enabled, any JSON field whose value is null will be wrapped in .nullable() in the schema (e.g. z.string().nullable()). This is the correct Zod way to declare that a field can be either its base type or null.
z.infer<typeof MySchema> extracts the TypeScript type from a Zod schema automatically. Instead of maintaining a separate interface and a schema in parallel, you define the schema once and let Zod derive the type. The exported type MySchema can then be used throughout your codebase for static type-checking.
.nullable() means the value can be null. .optional() means the field can be absent entirely (i.e. undefined or not present in the object). You can also chain both: z.string().nullable().optional(). The optional() toggle here applies .optional() to every field in the root object.
Absolutely. Zod is the default validation library for tRPC, and schemas generated here work directly as tRPC input/output validators. For Next.js API routes, you can call MySchema.parse(req.body) to validate and type-cast the request body in one step.
A JSON to Zod schema converter takes a raw JSON sample — the kind you'd copy from an API response, a Postman request, or a browser DevTools network tab — and generates the corresponding Zod schema code. Instead of hand-writing every z.object(), z.string(), and z.array() declaration, you paste your JSON and get production-ready TypeScript validation code in seconds.
Zod has become the de-facto runtime validation library for TypeScript projects, particularly in the Next.js, tRPC, and React Query ecosystem. Its killer feature is z.infer<typeof Schema>, which extracts a static TypeScript type directly from a schema definition — eliminating the need to maintain a separate interface or type that could drift out of sync with your actual validation logic.
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The converter walks your JSON recursively, inspecting the JavaScript type of each value:
z.string()z.number()z.boolean()z.null() (or wrapped with .nullable() if the nullable option is on)z.object({...}) with the same recursive logic applied to each propertyz.array(...) wrapping the inferred element schema (if the first element is an object, the full object schema is generated)The final output is wrapped in a const MySchema = z.object({...}) declaration, optionally preceded by an import { z } from 'zod'; statement and followed by a type MySchema = z.infer<typeof MySchema>; alias.
TypeScript types and interfaces are compile-time constructs — they vanish at runtime. If you declare type User = { id: number; name: string } and then cast an API response to it with response.json() as User, TypeScript will trust you even if the API sends garbage. Zod fixes this by validating the shape of the data at runtime with UserSchema.parse(data), throwing a descriptive error if the structure doesn't match.
The benefits compound quickly in larger projects:
.extend(), .merge(), .pick(), and .omit().These two modifiers address different scenarios and are often confused:
.nullable() — The value can be either its declared type OR null. Use this when an API field is explicitly set to null to indicate "no value". Example: z.string().nullable() accepts "Alice" or null..optional() — The key can be entirely absent from the object. The TypeScript type becomes string | undefined. Use this for fields that may not be present in every response variant. Example: z.string().optional() accepts "Alice" or a missing key.You can chain both for maximum flexibility: z.string().nullable().optional() accepts a string, null, or undefined/absent field.
Real-world API responses are rarely flat. Our generator handles arbitrary nesting depth. A JSON structure like:
{
"user": {
"profile": {
"avatar": { "url": "https://...", "size": 128 }
}
}
}…generates correctly nested Zod schemas with each object level wrapped in its own z.object(). Arrays of objects use the first element's structure to infer the element schema, which covers the vast majority of homogeneous API arrays.
Once you have your Zod schema, integrating it is straightforward:
const user = UserSchema.parse(await res.json()) — throws if the response shape is wrong, returns a fully typed object if it matches.const result = UserSchema.safeParse(data) — returns { success: true, data: ... } or { success: false, error: ... } without throwing, ideal for user input.input: UserSchema in your procedure definition.@hookform/resolvers/zod to wire the schema as a form resolver, getting per-field validation for free.The generator infers types from the values present in your sample. For the most accurate schema, provide a representative JSON sample that includes all possible fields, including those that can be null. For union types (fields that can be a string in one case and a number in another), you'll want to manually add z.union([z.string(), z.number()]) — automatic union detection from a single sample is outside the tool's scope. Similarly, string formats like dates, UUIDs, and emails are typed as plain z.string() — add .datetime(), .uuid(), or .email() refinements manually after generating the base schema.