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docs: improve intro sentence

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Johann Schopplich
2025-11-19 04:42:35 +01:00
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README.md
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[![npm downloads (total)](https://img.shields.io/npm/dt/@toon-format/toon.svg)](https://www.npmjs.com/package/@toon-format/toon)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](./LICENSE)
**Token-Oriented Object Notation** is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for *LLM input* as a lossless, drop-in representation of JSON data.
**Token-Oriented Object Notation** is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for *LLM input* as a drop-in, lossless representation of your existing JSON.
TOON combines YAML's indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays. TOON's sweet spot is uniform arrays of objects (multiple fields per row, same structure across items), achieving CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably. For deeply nested or non-uniform data, JSON may be more efficient.

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docs/guide/getting-started.md
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## What is TOON?
**Token-Oriented Object Notation** is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for *LLM input* as a lossless, drop-in representation of JSON data.
**Token-Oriented Object Notation** is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for *LLM input* as a drop-in, lossless representation of your existing JSON.
TOON combines YAML's indentation-based structure for nested objects with a CSV-style tabular layout for uniform arrays. TOON's sweet spot is uniform arrays of objects (multiple fields per row, same structure across items), achieving CSV-like compactness while adding explicit structure that helps LLMs parse and validate data reliably.