TOON MCP server reduces tool result tokens by 30-60% in OpenClaw

An MCP server has been released that automatically compresses structured JSON tool results into TOON format, a token-efficient representation designed to reduce token usage by 30-60% for tabular data in OpenClaw sessions.

How it works

TOON is a format designed for token-efficient LLM data representation that uses field names declared once, indentation instead of braces, and no redundant quoting. The MCP server compares token counts between TOON and compact JSON and returns whichever is smaller.

Setup

Add the MCP server to your OpenClaw config (~/.openclaw/openclaw.json):

{
  "mcpServers": {
    "toon": {
      "command": "npx",
      "args": ["@fiialkod/toon-mcp-server"]
    }
  }
}

Add a rule to your AGENTS.md: When any tool returns structured JSON data (arrays of objects, API responses, database results, logs) larger than ~20 fields, pass the result through the toon_format_response tool before reasoning over it.

Performance

For tabular data (arrays of uniform objects, emails, calendar events, search results, logs, DB rows), TOON typically wins by 30-60%. For small payloads or deeply nested configs, it falls back to JSON compact.

Benchmark with 15 financial transactions and 15 questions:

• JSON: 14/15 correct (93.3%), ~749 tokens used
• TOON: 14/15 correct (93.3%), ~398 tokens used

Same accuracy with 47% fewer tokens. The errors were on different questions and neither was caused by the format. TOON was lossless in tests — decode(encode(data)) === data.

Use cases

Best for: Gmail/Calendar MCP results, database queries, API responses, file listings, logs — anything that's an array of objects with repeated keys.

Not needed for: Small payloads (<5 items), deeply nested configs, data you need to pass back as raw JSON.

Problem it solves

System prompt and tool schemas have high fixed costs in OpenClaw, workspace files are semi-fixed, but tool results accumulate fast. When agents read files, query APIs, or browse, this pushes sessions into compaction where context is lost. TOON works upstream by shrinking tool results before they enter the transcript, delaying compaction and keeping more session history intact.