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TOON Converter - JSON to TOON Parser

Free online TOON converter tool. Convert JSON to TOON and TOON to JSON instantly. TOON format reduces AI token usage significantly for uniform data structures, lowering API costs for LLM applications.

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Input: JSON

Paste or upload your JSON data

Output: TOON

Converted TOON output

What is TOON Format?

TOON (Token-Oriented Object Notation) is a compact, human-readable encoding system designed specifically for LLM prompts. It represents the same data structures as JSON—objects, arrays, and primitives—but uses a more token-efficient syntax that can reduce token usage by approximately 40% while maintaining lossless conversion.

How to Use This Tool

  1. Paste or upload your JSON data in the input panel
  2. Click "Convert" to transform JSON to TOON format
  3. Use "Swap Mode" to switch between JSON→TOON and TOON→JSON conversion
  4. Copy or download the converted output for use in your LLM applications
  5. Try the "Load Example" button to see TOON format in action

Benefits of TOON Format

  • 40% Token Reduction: Significantly reduces AI token usage for uniform data structures
  • Lower API Costs: Save money on LLM API calls by using fewer tokens
  • Better LLM Comprehension: Improves model accuracy compared to compact JSON
  • Human Readable: Uses YAML-style indentation and CSV-like tabular layout
  • Lossless Conversion: Maintains complete data integrity when converting to/from JSON
  • Ideal for Uniform Arrays: Excels with structured data like database records

How TOON Works

TOON combines two structural approaches for optimal token efficiency:

  • YAML-style indentation: For nested objects, eliminates verbose bracket syntax
  • CSV-like tabular layout: For uniform arrays, field names appear once in a header, then each row contains only values
  • Explicit length declarations: Helps LLMs understand array boundaries
  • Minimal quoting: Reduces character overhead while maintaining clarity

When to Use TOON

TOON format is particularly effective for:

  • Database records and tabular data with consistent schemas
  • API responses with uniform array structures
  • LLM prompts containing structured data examples
  • Applications where reducing token count directly saves costs
  • Scenarios requiring both efficiency and human readability

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