How to use the JSON → Zod Schema
1. Paste a JSON object or array into the input — a real API response works best since the values help infer smart validators.
2. Set the schema name (used for the exported variable and inferred TypeScript type).
3. The Zod schema is generated instantly — copy it into your TypeScript project.
Tip: strings that look like emails, URLs, UUIDs, or ISO dates get smarter validators (.email(), .url(), .uuid(), .datetime()) automatically.
What is it?
Zod is the most popular TypeScript-first validation library, used heavily in Next.js, tRPC, React Hook Form, and Astro projects. It lets you define a schema and simultaneously get TypeScript type inference — write once, validate and type in one step.
This tool generates a complete Zod schema from any JSON sample. It uses smart heuristics to infer validators from the actual values: strings matching email patterns get z.string().email(), URLs get z.string().url(), ISO dates get z.string().datetime(), and integers get z.number().int(). Nested objects become nested z.object() schemas.
The generated code includes a z.infer type export so you have a fully typed TypeScript type derived from the schema — no duplication of type definitions.
Example
{"id": 1, "email": "[email protected]", "tags": ["dev"]}import { z } from "zod";
const UserSchema = z.object({
id: z.number().int(),
email: z.string().email(),
tags: z.array(z.string()),
});
export type User = z.infer<typeof UserSchema>;Email strings automatically get .email() validator. Use .parse() to validate or .safeParse() for error handling.
Benefits
Generate accurate code from your actual data
TypeScript interfaces, Python dataclasses, and OpenAI function schemas are generated directly from your real JSON — accurate field names, correct types, no guesswork.
Control LLM API costs before you scale
Token counts and per-call cost estimates for GPT-4o, Claude 3.5, and Gemini let you optimise prompt size and model choice before a feature goes to production.
Ready-to-run code and configs, not boilerplate
Every tool produces output you can paste directly into your project — API request code, prompt templates, chunk configurations — adapt rather than write from scratch.
Purpose-built for AI engineering workflows
From RAG chunking strategy to prompt template selection to function calling schema design — tools built for the specific problems AI engineers encounter daily.
Frequently asked questions
How do I use the generated schema to validate data?
Use UserSchema.parse(data) which throws on invalid data, or UserSchema.safeParse(data) which returns { success, data, error } — safe to use in API routes without try/catch.
How do I make fields optional in Zod?
Add .optional() to any field: z.string().optional() — this allows undefined. Use .nullable() for null values, or .nullish() for both null and undefined.
How does Zod work with tRPC?
tRPC uses Zod schemas as input validators for procedures. Define your schema with this tool and use it as: procedure.input(UserSchema).query(async ({ input }) => {...})
Can I use Zod for form validation with React Hook Form?
Yes — use @hookform/resolvers/zod: const { register, handleSubmit } = useForm({ resolver: zodResolver(UserSchema) }). The schema validates on submit and provides typed errors.
What does z.infer do?
z.infer<typeof UserSchema> extracts the TypeScript type that the schema validates. It's equivalent to manually writing a TypeScript interface but derived automatically from the schema definition, keeping both in sync.