LLM

JSON ↔ TOON Converter

Convert JSON to TOON outline notation to save tokens in LLM prompts.

Converters

JSON to TOON

Parse JSON safely and emit TOON with sorted keys for consistent diffs.

JSON input

Validation happens in-browser—no payloads leave the page.

TOON output

products[2]{id,in_stock,price,product_name,tags}:
  101,true,299.99,Wireless Noise Cancelling Headphones,[audio, bluetooth, premium]
  102,true,145.5,Ergonomic Mechanical Keyboard,[peripherals, office, typing]

TOON to JSON

Outline notation is parsed with the JSON-only schema to avoid surprises.

TOON input

Two-space indents keep hierarchies obvious for humans and models.

JSON output

{
  "products": [
    {
      "id": 101,
      "in_stock": true,
      "price": 299.99,
      "product_name": "Wireless Noise Cancelling Headphones",
      "tags": [
        "audio",
        "bluetooth",
        "premium"
      ]
    },
    {
      "id": 102,
      "in_stock": true,
      "price": 145.5,
      "product_name": "Ergonomic Mechanical Keyboard",
      "tags": [
        "peripherals",
        "office",
        "typing"
      ]
    }
  ]
}

Why TOON?

Token-lean structure: Dropping braces and commas typically trims prompt token count by 10–20%, leaving more space for actual content.
Indent-first clarity: Tree depth is obvious to humans and models, which reduces hallucinated siblings when an LLM rewrites data.
Type-disciplined parsing: The converter enforces JSON-safe scalars so TOON stays compatible with APIs and config files.

Why LLM teams like it

TOON is trending with LLM-heavy stacks because it reads like an outline, keeps deltas tiny for review, and round-trips cleanly to JSON for execution. It also plays well with constrained decoding strategies that expect deterministic indentation.

plan:
  summary: "Keep output terse and structured"
  steps:
    - capture goals
    - normalize inputs
    - stream final JSON

About JSON to TOON Converter

This converter transforms standard JSON into TOON (Tree Object Outline Notation), a specialized outline notation optimized for Large Language Models (LLM). It strips away redundant syntax like braces and quotes to create a highly compact prompt format.

Reduce your API costs and stay within context window limits. By compressing generic data structures into minimal token counts, you can feed more context to models like GPT-4 or Claude without sacrificing structural integrity. It is the perfect pre-processing step for prompt engineering.

Paste your JSON object to generate the TOON representation. Copy the output for your LLM prompt. The tool also supports reverse conversion, allowing you to turn LLM-generated outline text back into valid JSON.

Under the Hood

TOON (Tree Object Outline Notation) is a custom whitespace-sensitive format designed to minimize token usage for LLMs. The converter traverses the JSON AST, replacing braces and quotes with indentation and labeled nodes. Arrays are compressed into concise lists. This reduction often yields 20-30% token savings for deep trees compared to standard JSON, making it ideal for large context windows.