JSON Schema Builder

Build JSON schemas visually for LLM structured outputs, function calling, and API validation.

Schema title

Schema description

Structured output modeAdds stricter object schemas and backfills simple field descriptions.

Property name

Required

JSON example

Infer from JSON example

Existing schema

Import existing schema

Schema preview

2 top-level properties

{
  "type": "object",
  "properties": {
    "answer": {
      "type": "string",
      "description": "Primary answer from the model"
    },
    "confidence": {
      "type": "number",
      "description": "Confidence score"
    }
  },
  "additionalProperties": false,
  "required": [
    "answer"
  ]
}

How to Use This Tool

Add top-level properties manually, or start from a JSON example if you already know the response shape you want.
Mark required fields, add descriptions, and switch on structured output mode for stricter object schemas.
Use nested properties for objects and item properties for arrays of objects.
Paste an existing schema if you want to refine it visually without editing raw JSON first.
Copy the preview and use it in OpenAI structured outputs, Anthropic tool definitions, or your own validation layer.

Why a JSON Schema builder matters for AI apps

Structured output features are only as reliable as the schema behind them. If the schema is vague, overly permissive, or missing field descriptions, models are more likely to drift or produce edge-case failures.

A visual builder helps when you are iterating quickly. Instead of manually editing nested JSON and scanning for missing required fields, you can shape the contract at a higher level and keep the preview in sync as you work.

This tool is especially useful for response contracts that need to survive prompt changes. DeepSeek V4, GPT, Claude, and Gemini all support structured or tool-like outputs in different ways, so test output quality, cost, and format together instead of in isolation.

Last updated: April 2026

FAQ

What is JSON Schema used for in AI?

JSON Schema defines the expected structure of LLM outputs. OpenAI's structured outputs, Anthropic's tool use, and most function-calling APIs use JSON Schema to tell the model exactly what format to respond in.

What is 'LLM Structured Output Mode'?

This mode applies best practices for LLM schemas: it adds descriptions to all fields (helps the model understand what to generate), uses enums for constrained values, and avoids patterns that confuse models like deeply nested optional fields.

Can I import from a JSON example?

Yes! Paste a JSON example and the tool will infer the schema automatically. It detects types, required fields, and array item types. You can then refine the generated schema using the visual editor.

Should every field have a description?

For AI-facing schemas, usually yes. Field descriptions give the model more grounding about what to generate and reduce ambiguity when labels alone are not enough.

Why set additionalProperties to false?

Strict object schemas make downstream validation easier and reduce accidental keys in model responses. When you need a looser contract, you can still edit the generated JSON before using it.