JSON to Code Converter

Overview

JSON to Code analyzes your JSON structure and generates corresponding types, interfaces, classes, or schemas. This is a pure client-side tool with no AI - it uses deterministic parsing for consistent, fast results.

Key features:

• 6 output languages - TypeScript, Python, Go, Rust, Java, Zod
• Smart type inference - Detects numbers, booleans, dates, nulls
• Nested structure support - Handles deeply nested objects and arrays
• Naming conventions - Automatic case conversion per language
• Instant generation - No API calls, works offline

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Supported Languages

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How to Use

1. Paste JSON - Enter valid JSON in the input area
2. Select language - Choose from 6 output options
3. Set root name - Name your root type (default: "Root")
4. Click Convert - Instant code generation
5. Copy code - One-click copy to clipboard

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Example Input

{
  "id": 123,
  "name": "John Doe",
  "email": "john@example.com",
  "isActive": true,
  "roles": ["admin", "user"],
  "metadata": {
    "createdAt": "2024-01-01T00:00:00Z",
    "updatedAt": null,
    "loginCount": 42
  },
  "tags": ["premium", "verified"]
}

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Output Examples

TypeScript

interface Metadata {
  createdAt: string;
  updatedAt: string | null;
  loginCount: number;
}

interface User {
  id: number;
  name: string;
  email: string;
  isActive: boolean;
  roles: string[];
  metadata: Metadata;
  tags: string[];
}

Python (dataclass)

from dataclasses import dataclass
from typing import Optional, List

@dataclass
class Metadata:
    created_at: str
    updated_at: Optional[str]
    login_count: int

@dataclass
class User:
    id: int
    name: str
    email: str
    is_active: bool
    roles: List[str]
    metadata: Metadata
    tags: List[str]

Go

type Metadata struct {
    CreatedAt  string  `json:"createdAt"`
    UpdatedAt  *string `json:"updatedAt"`
    LoginCount int     `json:"loginCount"`
}

type User struct {
    ID       int      `json:"id"`
    Name     string   `json:"name"`
    Email    string   `json:"email"`
    IsActive bool     `json:"isActive"`
    Roles    []string `json:"roles"`
    Metadata Metadata `json:"metadata"`
    Tags     []string `json:"tags"`
}

Rust

use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize)]
pub struct Metadata {
    #[serde(rename = "createdAt")]
    pub created_at: String,
    #[serde(rename = "updatedAt")]
    pub updated_at: Option<String>,
    #[serde(rename = "loginCount")]
    pub login_count: i64,
}

#[derive(Debug, Serialize, Deserialize)]
pub struct User {
    pub id: i64,
    pub name: String,
    pub email: String,
    #[serde(rename = "isActive")]
    pub is_active: bool,
    pub roles: Vec<String>,
    pub metadata: Metadata,
    pub tags: Vec<String>,
}

Java

public class Metadata {
    private String createdAt;
    private String updatedAt;
    private int loginCount;

    public String getCreatedAt() { return createdAt; }
    public void setCreatedAt(String createdAt) { this.createdAt = createdAt; }

    public String getUpdatedAt() { return updatedAt; }
    public void setUpdatedAt(String updatedAt) { this.updatedAt = updatedAt; }

    public int getLoginCount() { return loginCount; }
    public void setLoginCount(int loginCount) { this.loginCount = loginCount; }
}

public class User {
    private int id;
    private String name;
    private String email;
    private boolean isActive;
    private String[] roles;
    private Metadata metadata;
    private String[] tags;

    // Getters and setters...
}

Zod Schema

import { z } from 'zod';

const MetadataSchema = z.object({
  createdAt: z.string(),
  updatedAt: z.string().nullable(),
  loginCount: z.number(),
});

const UserSchema = z.object({
  id: z.number(),
  name: z.string(),
  email: z.string(),
  isActive: z.boolean(),
  roles: z.array(z.string()),
  metadata: MetadataSchema,
  tags: z.array(z.string()),
});

type User = z.infer<typeof UserSchema>;

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Type Inference Rules

Nullable Fields

When a field contains null:

• TypeScript: string | null
• Python: Optional[str]
• Go: *string (pointer)
• Rust: Option<String>
• Java: String (nullable by default)
• Zod: z.string().nullable()

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Naming Convention Conversion

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Technical Details

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Tips for Best Results

1. Valid JSON only - Tool validates input before processing
2. Consistent arrays - Arrays with mixed types become any[]
3. Name your root - Default "Root" can be renamed
4. Sample data - Use representative sample, not full dataset
5. Check nullables - Include null values in sample to get Optional types

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Use Cases

API Integration

Convert API response JSON to types for type-safe client code:

curl https://api.example.com/user/123 | pbcopy
# Paste JSON → Get TypeScript interface

Database Modeling

Generate ORM models from JSON representations of your data.

Configuration Typing

Create typed configs from JSON configuration files:

// config.json → ConfigSchema type
{
  "port": 3000,
  "database": { "host": "localhost", "port": 5432 }
}

Form Validation

Generate Zod schemas for runtime form validation in React.

Testing

Create mock data types from API example responses.

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Privacy & Security

• 100% client-side - No data sent to servers
• No storage - JSON not persisted
• No tracking - Input not logged
• Works offline - No internet required after page load