LLM JSON Schema Generator
Generate JSON Schema for LLM structured outputs — OpenAI, Anthropic, Gemini, Ollama.
Free online LLM JSON Schema generator. Paste a JSON example and instantly get a JSON Schema (Draft 7 / 2019-09 / 2020-12) optimized for structured LLM outputs. Generate provider-specific wrappers for OpenAI response_format, Anthropic tool use input_schema, Gemini function calling parameters, and Ollama. Options for strict mode, require all properties, and auto-descriptions. Runs entirely in your browser — your data never leaves your machine.
Keywords: llm json schema, structured output schema, openai json schema, openai response format, claude structured output, anthropic tool schema, gemini function calling, ollama json, json schema generator, ai json schema, structured output generator, json schema draft 7, json schema 2020-12, json schema from json
Tags: json-schema, llm, openai, anthropic, gemini, structured-output, generator, ai
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LLM JSON Schema Generator is also known as: Structured Output Schema Generator, AI JSON Schema Builder.
How to LLM JSON Schema Generator Online
Paste a JSON example into the input area, or switch to DSL mode to describe your schema.
Select your target LLM provider (OpenAI, Anthropic, Gemini, Ollama) or "None" for raw JSON Schema.
Choose the JSON Schema draft version (2020-12 recommended for OpenAI structured outputs).
Configure options: root schema name, require all properties, strict mode, auto-descriptions.
Click Generate or press ⌘↵ to create your provider-specific schema.
Copy the generated schema and paste it into your LLM API call.
LLM JSON Schema Generator Features
Generate JSON Schema optimized for LLM structured outputs with provider-specific wrappers.
OpenAI support: generates response_format with json_schema type for GPT-4o and later.
Anthropic support: generates tool use input_schema for Claude models.
Gemini support: generates function calling parameters for Gemini models.
Ollama support: generates JSON format schemas for local LLM inference.
Draft version selection: Draft 7, Draft 2019-09, and Draft 2020-12.
Require all properties: forces every property into the required array for guaranteed field presence.
Strict mode: sets additionalProperties: false on all objects to prevent LLM hallucinations.
Auto-descriptions: auto-generates description fields from key names for better LLM context.
Array item detection: properly extracts nested array element types as named definitions.
DSL mode: describe schemas with simple TypeScript-like syntax instead of JSON examples.
Stats display: shows property count, nesting depth, and required field count.
Shareable URLs: gzip-compress your input and settings into a shareable link.
Works entirely in your browser — your data never leaves your machine.
Supported Formats & Dialects
The LLM JSON Schema Generator supports 5 syntax formats and dialects for accurate parsing and processing.
- OpenAI
- response_format with json_schema type for GPT-4o and later structured outputs.
- Anthropic
- Tool use input_schema for Claude models with structured output support.
- Gemini
- Function calling parameters for Gemini 1.5 and later models.
- Ollama
- JSON format mode for local LLM inference with schema constraints.
- Raw JSON Schema
- Standard JSON Schema for any LLM or tool that accepts JSON Schema directly.
In-Depth Technical Guides
All GuidesGenerating Python Pydantic V2 Models from JSON: Schemas, Field Validators & Nested Types
Convert raw JSON payloads and schemas into robust, type-safe Python Pydantic V2 models. Master Field constraints, custom validation, and nested structures.
LLM API Cost Optimization: Prompt Caching, Model Routing & Token ROI
Slash your OpenAI, Anthropic, and Gemini API bills by 60–80%. Master prompt caching, tiered model routing, token budgeting, and batch processing economics.
LLM Token Counting & Context Window Budgeting: BPE Tokenizers & Cost Optimization
Understand BPE tokenization, calculate prompt vs output token consumption, utilize prompt caching, and budget context windows to optimize AI inference costs.
Mastering LLM Structured Outputs: Enforcing JSON Schema in Production
Master structured outputs with LLMs using JSON Schema Draft 2020-12, OpenAI strict schemas, Anthropic tool definitions, and token-optimized validation pipelines.
Model Context Protocol (MCP) Server Schema Validation & Tool-Calling Debugging
A technical guide to authoring, validating, and debugging JSON Schema tool definitions for Model Context Protocol (MCP) servers in AI agents and LLM applications.
Prompt Injection Scanning: A Practical Security Checklist
A developer security guide to identifying direct, indirect, and multi-turn prompt injection vulnerabilities in LLM applications and agent pipelines.
Related Standards & RFC Specifications
All StandardsJSON Schema is a declarative JSON dialect for annotating, validating, and establishing structural contracts for JSON documents and REST APIs.
Prompt engineering is the practice of structuring, refining, and designing inputs for Large Language Models to maximize accuracy, consistency, and alignment.
Prompt injection is an AI security vulnerability where untrusted inputs subvert system instructions, leading to data exfiltration, jailbreaks, or unauthorized tool calls.
Explore Full AI Model Pricing Directory
Compare per-token rates, prompt caching discounts, and context windows across leading LLMs (GPT-4o, Claude 3.5 Sonnet, Gemini 2.5 Flash, DeepSeek, and more) in our verified catalog.
Pre-built Automation Pipelines
Chain LLM JSON Schema Generator with other utilities in a multi-step visual workflow.
Base64 Decode → JSON Format
Decode a Base64 string and pretty-print the JSON inside it.
CSV → JSON → YAML
Convert CSV data to JSON, then to YAML format.
JSON Format → TypeScript Schema
Format JSON and generate TypeScript/Zod schema from it.
Example Input & Output
Sample ReferenceInteractive Example: LLM JSON Schema Generator in ActionShow example
Sample Input (text)
{"id":1,"name":"Alice Smith","email":"[email protected]","isActive":true,"score":95.5}Sample Output
[Processed output for LLM JSON Schema Generator]
Input transformed successfully using llm json schema, structured output schema, openai json schema.What happened:
Generate JSON Schema for LLM structured outputs — OpenAI, Anthropic, Gemini, Ollama.
Frequently Asked Questions
- How do I use this with OpenAI structured outputs?
- Select "OpenAI" as the provider and choose Draft 2020-12. The tool generates a response_format object with the json_schema wrapper. Paste the output into your API call as response_format: { type: "json_schema", json_schema: { name: "YourSchema", schema: {...}, strict: true } }.
- What's the difference between JSON mode and JSON Schema mode in OpenAI?
- JSON mode (response_format: { type: 'json' }) lets the model return any valid JSON but doesn't enforce structure. JSON Schema mode (response_format: { type: 'json_schema', json_schema: {...} }) guarantees the model outputs JSON matching your exact schema - including required fields and types.
- Which JSON Schema draft should I use?
- For OpenAI, Anthropic, and Gemini, use Draft 2020-12 as it supports $defs for clean schema organization. Draft 2019-09 is useful for intermediate compatibility. Draft 7 is for legacy systems.
- How does Anthropic structured output work?
- Anthropic Claude models support structured output via tool use. Generate a schema with the Anthropic provider, then use the output as the input_schema in your tool definitions. Claude will output JSON matching the schema.
- Does this work with Ollama / local LLMs?
- Yes. Select "Ollama" as the provider and use the generated schema with Ollama JSON mode. The wrapper includes format: "json" and the schema for compatible models like those with function calling support.
- Why should I set strict mode?
- Strict mode sets additionalProperties: false on all objects, preventing the LLM from inventing fields not defined in your schema. This is critical for production applications where downstream code expects an exact shape.
- Is my data safe?
- Yes, all generation runs entirely in your browser using JavaScript. No JSON data is ever transmitted to a server, making this tool safe for sensitive data.
- What is the DSL mode?
- DSL mode lets you describe schemas using a simple TypeScript-like syntax: { name: string, age: number, tags: string[] }. This is useful when you want to define a schema structure without providing a full JSON example.
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