Toon Token Optimizer

What is Toon Token Optimizer?

Toon Token Optimizer is an MCP (Model Context Protocol) server for TOON (Token-Oriented Object Notation), a compact data format that reduces LLM token usage by 50–70% when sending structured data. It provides tools to encode, decode, analyze, and optimize prompts for token efficiency.

How to use Toon Token Optimizer?

Install via npx (no install needed) by adding the server to your MCP settings, or globally with npm install -g toon-mcp-server. Then invoke tools like toon_encode, toon_decode, toon_analyze, or toon_optimize_prompt through your MCP client (e.g., Claude Desktop or Claude Code).

Key features of Toon Token Optimizer

• Convert JSON, CSV, TSV, XML, HTML tables, and YAML to TOON format
• Decode TOON back to JSON
• Analyze data to show potential token and cost savings
• Automatically find and convert data sections in prompts
• Achieve up to 65% token reduction for structured data

Use cases of Toon Token Optimizer

• Sending large arrays of objects (tables, lists, records) to an LLM
• Optimizing API responses or database results for cost efficiency
• Reducing prompt size for high-volume LLM requests
• Benchmarking token savings across different data formats and volumes

FAQ from Toon Token Optimizer

What is an MCP server and how does it interact with my LLM client?

This server implements the Model Context Protocol, allowing LLM clients like Claude Desktop or Claude Code to call its tools (e.g., toon_encode) via stdio. No separate network service is required.

What are the system requirements?

Node.js is required to run the package. It is available on npm and can be used with any MCP‑compatible client.

Which input formats does toon_encode support?

It supports JSON, CSV, TSV, XML, HTML tables, and YAML.

Is TOON effective for all types of data?

It works best on arrays of objects with the same structure (tables, lists, records). It is less effective for small payloads (<5 items) or deeply nested, non‑uniform data.

How much can I save with Toon Token Optimizer?

Benchmarks show 50–65% token savings depending on dataset size. At scale, cost savings can be thousands of dollars per million requests.