What is JSON to TypeScript?
JSON to TypeScript is a browser-based code generator that reads a JSON sample and produces matching TypeScript interfaces. Nested objects are extracted into their own named interfaces, arrays of objects are merged into a single shape, and fields that do not appear in every element are marked optional — so the output reflects the real structure of your data.
When you consume a JSON API in a TypeScript project, you need type definitions that describe the response so the compiler can catch mistakes and your editor can autocomplete field names. Writing those interfaces by hand from a sample response is tedious and easy to get wrong, especially for deeply nested payloads. This tool automates that first draft in a single paste.
The types are inferred from the specific sample you provide, so they describe the data you pasted — not necessarily every value the field could ever hold. Everything runs locally in your browser; your JSON, which may be a real API response with sensitive fields, is never uploaded.
Why use JSON to TypeScript?
Hand-writing interfaces for a large API response is slow and error-prone — a mistyped field name or a missed nested object leads to type errors that only surface later. Generating the interfaces from a real sample gives you an accurate starting point in seconds, which you can then refine rather than build from scratch.
The tool handles the tricky cases automatically: it names nested interfaces, merges arrays of similar objects into one shape, and marks fields optional when they are not present across all elements. That merging behavior is exactly what you would do manually, but instant and consistent.
Doing this locally keeps your data private. API responses used as samples frequently include user data, tokens, or internal identifiers. This tool processes everything in the browser and never sends your JSON anywhere, which you can confirm in the Network tab.
Features
- Generate TypeScript interfaces from any JSON sample
- Extracts nested objects into their own named interfaces
- Merges arrays of objects into a single shape
- Marks fields optional when they are missing in some elements
- Customizable root interface name
- One-click copy of the generated types to your clipboard
- Download the result as a .ts file
- Runs entirely in your browser — no uploads, works offline
How to use JSON to TypeScript
- Paste a representative JSON sample — ideally a real API response — into the input panel on the left.
- Optionally set the root interface name (it defaults to Root) to something meaningful like ApiResponse or User.
- The generated TypeScript interfaces appear in the right panel automatically as you type.
- Review the output, especially optional fields and inferred types, and adjust your source sample if needed.
- Copy the interfaces to your clipboard or download them as a .ts file to drop into your project.
Example 1 — Simple object
Paste a flat JSON object to get a single interface with correctly-typed fields.
Input
{"name": "Ada", "age": 30, "active": true}Output
interface Root {
name: string;
age: number;
active: boolean;
}Example 2 — Nested object becomes its own interface
Nested objects are extracted into separate named interfaces and referenced from the parent.
Input
{"user": {"id": 1, "email": "a@b.com"}}Output
interface Root {
user: User;
}
interface User {
id: number;
email: string;
}Common Mistakes
- Trusting types from a tiny or unrepresentative sample: the generated types describe only the sample you paste. If a field is null or absent in your sample but can hold a string elsewhere, the inferred type will be wrong. Use a sample that exercises the full shape.
- Assuming null means the real type: a field that is null in the sample cannot have its true type inferred, so it may come out as any or null. Replace nulls with representative values before generating, then widen the type yourself.
- Ignoring optional fields: when arrays of objects disagree on which keys are present, the tool marks the differing fields optional. Do not remove the optional markers without confirming the field really is always present.
- Expecting format-level validation: TypeScript types describe shape (string, number, object), not formats. A field typed as string could be an email, a date, or a URL — the type will not enforce that distinction.
- Number precision assumptions: all JSON numbers become the TypeScript number type, which cannot safely represent integers larger than 2^53. For large IDs, consider typing them as string in your source data.
- Reusing generated names blindly: identical-looking nested objects in different parts of the tree get separate interfaces. Rename and deduplicate them to match your domain model.
Developer Tips
- Feed the tool a real API response that includes every field and a populated example of each array, so the inferred optionality and types match production reality.
- Set a meaningful root name upfront (like UserResponse) so the generated interface fits naturally into your codebase without a rename pass.
- For fields that are null in your sample, temporarily substitute a real value to get the correct inferred type, then restore null and widen to a union like string | null.
- Type large numeric identifiers as string in your JSON sample before generating, to avoid TypeScript number precision problems with values beyond 2^53.
- Run the JSON through the JSON Validator or JSON Formatter first to confirm it parses cleanly — invalid JSON cannot be converted.
Frequently Asked Questions
- How accurate are the generated types?
- The types are inferred directly from the JSON sample you provide, so they accurately describe that sample. They are only as complete as your data: if a field is missing, null, or an empty array in the sample, the tool cannot know its true type across all cases. For the best results, paste a representative response that includes every field with realistic, populated values, then refine edge cases like nullable fields by hand.
- Why are some fields marked optional with a question mark?
- When you paste an array of objects, the tool merges them into a single interface. If a particular key appears in some objects but not others, it cannot be guaranteed to always exist, so it is marked optional with a ? to reflect that uncertainty. This mirrors how real API responses often omit fields. Review these optional markers against the API documentation to decide whether the field is truly optional or just absent from your sample.
- What happens to null values?
- A null value carries no type information — there is no way to tell from null alone whether the field is normally a string, a number, or an object. As a result, null fields cannot be typed precisely and typically come out as any or null. The best practice is to substitute a representative non-null value in your sample to get the correct base type, then manually widen it to a union such as string | null in your final types.
- Is my JSON uploaded to a server?
- No. All type generation happens locally in your browser using JavaScript. Your JSON — which is often a real API response containing user data, tokens, or internal identifiers — is never sent to a server, logged, or stored. You can verify this by opening your browser's Network tab while generating: there are zero outbound requests, and the tool works even offline.
- How are nested objects and arrays handled?
- Nested objects are extracted into their own named interfaces and referenced from the parent, keeping the output modular and readable. Arrays of objects are analyzed and merged into a single element interface, with optional fields where the objects disagree. Arrays of primitives become typed arrays like string[] or number[]. This mirrors how you would structure the types by hand, but consistently and instantly.
- Can it produce types for very large or deeply nested JSON?
- Yes. The generator handles large, deeply nested payloads and everything runs in the browser, so nothing is uploaded regardless of size. Very large samples produce correspondingly large sets of interfaces, which you will likely want to split across files and rename to match your domain. For huge documents, generating from a trimmed but structurally complete sample often gives cleaner, more maintainable output.