@elminson
基本情報
MCP server for TOON (Token-Oriented Object Notation) - Reduce LLM token usage by 50-70%
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"toon": {
"command": "npx",
"args": [
"-y",
"toon-mcp-server"
]
}
}
}Convert data to TOON (Token-Oriented Object Notation) format to reduce token usage by 50-70%. Supports: JSON, CSV, TSV, XML, HTML tables, YAML, and raw objects. TOON uses a header-based format where field names are defined once: - JSON: {"id":1,"name":"test"} → [id,name]\n1,test - Reduces repetitive keys in arrays of objects Use this before sending large datasets to LLMs to save tokens and costs.
Decode TOON format back to JSON object or array.
Analyze data and show potential token savings with TOON encoding.
Optimize a prompt containing data for token efficiency. Detects data structures within the prompt and converts them to TOON format, adding instructions for the LLM to understand the format. Returns the optimized prompt with estimated token savings.
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.
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).
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.
Node.js is required to run the package. It is available on npm and can be used with any MCP‑compatible client.
It supports JSON, CSV, TSV, XML, HTML tables, and YAML.
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.
Benchmarks show 50–65% token savings depending on dataset size. At scale, cost savings can be thousands of dollars per million requests.
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