json-toon-parser

Version: 1.1.6 • License: MIT • Format: TOON

High-efficiency conversion and analysis toolkit: JSON → TOON with CLI, API, and Web UI server for real‑time token savings. Ideal for reducing LLM prompt payload size and optimizing costs.

Installation

Global (CLI + Web UI):
``bash
npm install -g json-toon-parser
`
Project (Programmatic API):
`bash
npm install json-toon-parser
`

Quick Start – Web UI

Start the local server:
`bash
json-toon serve
`
Browse to: http://localhost:3000 → paste JSON → convert → copy/download TOON.

CLI Usage

`bash

Start web UI server (recommended for easy usage)

json-toon serve

Or with custom port:

json-toon serve --port 8080

Compare JSON with TOON format

json-toon compare data.json

Convert JSON to TOON

json-toon convert data.json output.toon

Update JSON and generate comparison

json-toon update data.json
`

Programmatic Usage

`javascript
import { compare, convertFile, jsonToToon } from 'json-toon-parser';

// Compare JSON data
const result = compare({ name: "John", age: 30 }, { saveToFile: true });
console.log(result);
// Output: { jsonTokens: 15, toonTokens: 8, savings: 7, savingsPercentage: 46.67 }

// Convert file
const outputPath = convertFile('input.json', 'output.toon');
console.log(Saved to: ${outputPath});

// Convert JSON to TOON string
const toonString = jsonToToon({ hello: "world" });
`

Features

- 🌐 Web UI – Responsive, modern conversion dashboard
- ✅ Direct JSON → TOON conversion (string or file)
- 📊 Token efficiency metrics (JSON vs TOON counts, savings %)
- 💾 Timestamped output file generation
- 🔧 CLI commands: serve, compare, convert, update
- 📦 Programmatic API with types & tree-shakeable exports
- ⚡ Real-time stats + sample datasets in UI
- 🧪 GPT-style token counting via gpt-3-encoder

API (Summary)

json-toon-parser


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🚀 json-toon-parser — JSON → TOON converter, token optimizer, and developer toolkit

Convert verbose JSON into compact TOON, reduce LLM token usage, and measure savings.

Perfect for teams and engineers integrating large structured payloads into ChatGPT/OpenAI/Claude prompts or storage pipelines.

Official NPM package: https://www.npmjs.com/package/json-toon-parser

Documentation: docs/API.md

================================================================================

Quick Install

Global (CLI + Web UI):

`bash
npm install -g json-toon-parser
`

Programmatic (project):

`bash
npm install json-toon-parser
`

Verify:

`bash
json-toon --version
json-toon serve
`

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Why TOON? — The Value Proposition

TOON is a compact, human-friendly serialization optimized for token-efficient LLM prompts. It removes syntactic noise (quotes, braces, commas), reduces repeated keys, and uses concise array and nesting notation — which directly translates to lower API costs and faster transmission.

- Reduce per-request token counts by 40–70% (depends on structure)
- Keep payloads human-readable and reversible via the TOON library
- Ideal for prompts, telemetry, logs, and mobile/edge transfers

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Features

- CLI: serve, compare, convert, update
- Web UI: interactive editor, samples, copy & download
- Programmatic API (TypeScript types included)
- Token analysis using gpt-3-encoder for GPT-style counts
- Save TOON output with timestamped filenames

================================================================================

Usage Examples

CLI

`bash

Start web UI

json-toon serve

Compare & save TOON

json-toon compare data/input.json --save

Convert file

json-toon convert data/input.json output.toon

Update JSON and produce comparison

json-toon update data/input.json
`

Programmatic

`ts
import { compare, jsonToToon } from 'json-toon-parser';

const data = { users: [{ id: 1, name: 'Alice' }] };
const toon = jsonToToon(data);
const result = compare(data, { saveToFile: false });
console.log(result.toonOutput);
`

================================================================================

Example Comparison

Use this example to demonstrate typical savings — include it in docs, blog posts, or README highlights:

`
============================================================
COMPARISON RESULTS
============================================================
JSON Tokens: 24890
TOON Tokens: 10204
Token Savings: 14686
Efficiency: 59.00% reduction
Output File: output/data_DateTime.toon
============================================================
`

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When TOON Is Most Effective

- Nested JSON with many structural characters
- Large arrays of similar objects
- Repetitive keys repeated across objects
- Prompts that include large contexts for LLMs

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Performance Notes

Token counting is approximated with gpt-3-encoder. Results may vary slightly by tokenizers used by different LLM providers, but the relative savings are consistent.

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Links & Resources

- NPM: https://www.npmjs.com/package/json-toon-parser
- TOON format: https://github.com/bhushan020/json-toon-parser
- API docs: docs/API.md

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Contributing

See CONTRIBUTING.md` to get started. We welcome PRs, issues, and ideas.

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License

MIT © Bhushan Chaudhari

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