JSON Schema Generator
Infer a JSON Schema (draft 2020-12) from a JSON sample
Ideal for LLM structured output (OpenAI/Anthropic tool calling), API validation with ajv, or documenting a payload. All fields in the sample are marked required - remove any that are optional.
Nasıl çalışır
- 1Paste a JSON sample
Drop a representative object or array - an API response, a config, an LLM function call payload.
- 2Name the root type
Give the schema a title (defaults to 'RootSchema'). Used by generators and error messages.
- 3Copy the schema
Paste directly into your LLM tool definition, ajv, or API docs.
JSON Schema Generator Hakkında
Free online JSON Schema generator. Paste a JSON sample to instantly get a draft-2020-12 JSON Schema with types, required fields, and nested object/array shapes. Ideal for LLM structured output (OpenAI/Anthropic tool calling), ajv validation, and API contracts. 712 Tools üzerindeki JSON Schema Generator tamamen browser'ınızda native JavaScript API'lerle çalışır - hiçbir server verilerinizi görmez. Yani anında sonuç, tam privacy ve upload limiti yok.
İster bir debugging session olsun, ister hızlı bir sanity check ya da bir production incident'ı - bu tool istediğiniz kadar ücretsiz kullanılabilir. Watermark yok, kayıt yok, tool içinde reklam yok.
Sık sorulan sorular
Which JSON Schema draft?
Draft 2020-12 - the current version, and what OpenAI and Anthropic expect for structured output / tool calling.
Are all fields marked required?
Yes - every field in your sample. Remove the ones that are conditional from the required array by hand.
How are arrays typed?
The tool merges all items and produces one items schema. Mixed arrays become a union of types.
Is my JSON sent anywhere?
No. Inference happens entirely in your browser.
Devamını oku
How to write better AI prompts that actually work (ChatGPT, Claude, Gemini in 2026)
Prompt engineering isn't magic - it's a small set of structural moves that produce dramatically better output from any modern LLM. Here's the shape, the anti-patterns, and the template.
LLM token counting and API cost estimation: a 2026 developer's guide
Tokens aren't words, GPT-4o and Claude count them differently, and a 1M-context prompt can cost $30. Here's how tokens actually work, and how to estimate cost before you ship.
JSON Schema for LLM structured output: the developer's shortcut
OpenAI's structured output and Anthropic's tool use both want JSON Schema. Here's the draft that works everywhere, the fields models care about, and how to write one from an example in 10 seconds.
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