Data Validator

Data Validator

Generates precise validation schemas — JSON Schema, Pydantic models, Zod schemas, TypeScript types, or database constraints — from sample data, descriptions, or existing types to enforce data contracts at API boundaries, configuration files, and service interfaces.

When to Use

• User provides sample JSON/YAML and asks to "create a schema for this"
• API endpoints accept unvalidated request bodies
• User asks to generate Pydantic models, Zod schemas, or JSON Schema
• TypeScript types exist but need runtime validation added
• Configuration files need validation before the app starts
• Data pipelines need input/output contracts enforced
• User asks to convert between schema formats (JSON Schema → Zod, etc.)

Process

1. Identify the target schema format from context or ask:

   • JSON Schema (draft-07 / 2020-12) — language-agnostic, OpenAPI, configs
   • Pydantic v2 — Python, FastAPI, data validation + serialization
   • Zod — TypeScript/JavaScript, runtime type-safe validation
   • Yup — JavaScript, form validation, React integrations
   • Joi — Node.js, server-side validation
   • TypeBox — TypeScript, JSON Schema + TypeScript types in sync
   • class-validator + class-transformer — NestJS, decorators
   • OpenAPI requestBody schema — API spec validation

2. Analyze the input (sample data, description, or existing type):

   • Identify all fields and their data types
   • Infer required vs. optional fields from multiple samples or description
   • Determine string formats: email, UUID, URL, date-time, ISO 8601
   • Determine numeric constraints: min, max, integer vs. float
   • Identify array item types and length constraints
   • Identify enum/union types from repeated patterns
   • Note nullable vs. undefined vs. missing field semantics

3. Design the schema:

   • Use the most precise constraints possible (don't use string when email or uuid is more accurate)
   • Mark required fields explicitly; make optional fields clear
   • Add minimum/maximum for numbers where semantics imply bounds (age: 0–150, port: 1–65535)
   • Use minLength/maxLength for strings with natural bounds
   • Use additionalProperties: false in JSON Schema for strict objects
   • Use discriminated unions for polymorphic types

4. Add validation messages where the framework supports it (Zod, Yup, Pydantic):

   • Provide human-readable error messages for each constraint
   • Include field name and the constraint that failed

5. Generate the schema with documentation:

   • Add inline comments or field-level descriptions
   • Include example values where supported

6. If converting between formats, ensure semantic equivalence and note any features not supported in the target format.

Output Format

Pydantic v2 (Python)

from pydantic import BaseModel, Field, EmailStr, field_validator
from typing import Optional
from uuid import UUID
from datetime import datetime
from enum import Enum

class UserRole(str, Enum):
    admin = "admin"
    viewer = "viewer"
    editor = "editor"

class CreateUserRequest(BaseModel):
    model_config = {"str_strip_whitespace": True}

    name: str = Field(min_length=1, max_length=100, description="Full display name")
    email: EmailStr = Field(description="Primary email address")
    role: UserRole = Field(default=UserRole.viewer)
    age: Optional[int] = Field(default=None, ge=0, le=150)

    @field_validator('name')
    @classmethod
    def name_must_not_be_blank(cls, v: str) -> str:
        if not v.strip():
            raise ValueError('Name must not be blank or whitespace only')
        return v

Zod (TypeScript)

import { z } from 'zod';

const UserRole = z.enum(['admin', 'viewer', 'editor']);

export const CreateUserSchema = z.object({
  name: z.string().min(1, 'Name is required').max(100, 'Name too long').trim(),
  email: z.string().email('Invalid email address'),
  role: UserRole.default('viewer'),
  age: z.number().int().min(0).max(150).optional(),
});

export type CreateUserRequest = z.infer<typeof CreateUserSchema>;

Examples

Example Input

{
  "orderId": "ord_abc123",
  "customerId": "cus_xyz456",
  "items": [
    { "productId": "prod_001", "quantity": 2, "unitPrice": 29.99 }
  ],
  "shippingAddress": {
    "street": "123 Main St",
    "city": "Springfield",
    "country": "US",
    "postalCode": "62701"
  },
  "createdAt": "2024-03-15T10:30:00Z"
}

Example Output (JSON Schema)

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "required": ["orderId", "customerId", "items", "shippingAddress", "createdAt"],
  "additionalProperties": false,
  "properties": {
    "orderId": { "type": "string", "pattern": "^ord_[a-z0-9]+$" },
    "customerId": { "type": "string", "pattern": "^cus_[a-z0-9]+$" },
    "items": {
      "type": "array", "minItems": 1,
      "items": {
        "type": "object",
        "required": ["productId", "quantity", "unitPrice"],
        "additionalProperties": false,
        "properties": {
          "productId": { "type": "string" },
          "quantity": { "type": "integer", "minimum": 1 },
          "unitPrice": { "type": "number", "minimum": 0, "exclusiveMinimum": 0 }
        }
      }
    },
    "shippingAddress": {
      "type": "object",
      "required": ["street", "city", "country", "postalCode"],
      "properties": {
        "street": { "type": "string", "minLength": 1 },
        "city": { "type": "string", "minLength": 1 },
        "country": { "type": "string", "pattern": "^[A-Z]{2}$" },
        "postalCode": { "type": "string" }
      }
    },
    "createdAt": { "type": "string", "format": "date-time" }
  }
}

Boundaries

• Do NOT infer required fields from a single sample — ask for multiple examples or an authoritative description to distinguish required from optional.
• Do NOT use any / object / unknown types without noting that they weaken the contract.
• Do NOT generate validation that strips or transforms data unless the user explicitly requests coercion (i.e., prefer strict validation over silent mutation).
• If converting between schema formats, note any expressiveness gaps (e.g., JSON Schema contentMediaType has no direct Zod equivalent).
• Do NOT generate schemas that accept user-controlled data without length constraints — always bound strings and arrays.
• Pydantic v2 syntax differs significantly from v1 — confirm the version before generating code.