MCP 도구 검색은 10,000개의 토큰을 먹습니다. 350으로 줄였습니다.

I use MCP servers with Claude Code every day. Last week I actually counted how many tokens get burned just on tool discovery.

5 servers, 96 tools total. The JSON listing: 2,034 tokens. On a 128K context window, that’s before I’ve asked a single question.

20 tool calls later, each wrapped in {"content":[{"type":"text","text":"..."}]} — another 40,000 tokens of overhead. Brackets, quotes, commas, repeated {"type":"object","properties": declarations.

I wrote mcptoon to deal with this. It’s a CLI client that outputs TOON (Token-Optimized Object Notation) instead of JSON. The idea is dumb on purpose: the LLM doesn’t need {"type":"object","properties": to understand what a tool does. It just needs the tool name and the relevant fields.

pip install mcptoon

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Zero dependencies. 50KB. Python 3.10+. Windows, macOS, Linux.

What TOON looks like

Here’s tool discovery from 2 MCP servers:

JSON (287 tokens) — what every MCP client returns:

[
  {"name": "search_web", "description": "Search the web for information",
   "inputSchema": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}, "num_results": {"type": "number", "default": 5}}, "required": ["query"]}},
  {"name": "fetch_url", "description": "Fetch content from a URL",
   "inputSchema": {"type": "object", "properties": {"url": {"type": "string"}}, "required": ["url"]}}
]

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TOON (5 tokens) — what mcptoon returns:

search_web fetch_url

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For tool discovery, the agent just needs to know what tools exist. Not the full schema every single time.

When it does need the schema, TOON with full details is still 60% smaller:

name:search_web|description:Search_the_web|inputSchema:type:object|properties:query:type:string|description:Search_query|num_results:type:number|default:5|required:query||
name:fetch_url|description:Fetch_content_from_a_URL|inputSchema:type:object|properties:url:type:string|required:url

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Measured results

Operation JSON tokens TOON tokens Saved Tool discovery (96 tools) 2,034 62 97% Tool result (structured) 812 354 56% Tool result (raw HTML) 1,023 912 11%

Real session: 5 servers, 20 tool calls. JSON overhead was 47,200 tokens. With mcptoon: 8,100 tokens. That’s 39,100 tokens back.

How it strips tokens

JSON TOON What changed {"name":"search","count":3} `name:search\ count:3` [1, 2, 3] 1 2 3 Spaces instead of brackets+commas true / false T / F 1 char vs 4-5 null ∅ 1 symbol vs 4 "line1\nline2" line1↲line2 ↲ instead of escape sequence

I tested this pretty thoroughly — Claude, GPT-4, and Gemini all parse TOON output correctly. The structure is recoverable from the compact form. What’s not recoverable is the 1,900 tokens you spent on {"type":"object","properties": repeated 96 times.

Quick start

pip install mcptoon
mcptoon init
mcptoon add fetch --stdio npx -y @modelcontextprotocol/server-fetch
mcptoon manifest --toon
# → fetch:fetch

mcptoon call fetch fetch '{"url":"https://example.com"}' --toon

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Set MCPTOON_AGENT_TYPE=claude and it auto-selects --toon on every call.

Works with every agent

mcptoon is a CLI tool. If your agent runs shell commands, it can use it. No SDK, no plugin.

Agent Setup Claude Code mcptoon in SKILL.md Codex mcptoon in AGENTS.md Cursor mcptoon in .cursorrules OpenCode mcptoon in custom commands CatPaw mcptoon in skill files

One config file (~/.mcptoon/config.json), every agent shares it. Add a server, all agents see it instantly.

Safety

Dangerous operations get blocked by default:

$ mcptoon call db delete_table '{"name":"users"}'
Error [CONFIRMATION_REQUIRED]: Dangerous operation needs confirmation

$ mcptoon call db delete_table '{"name":"users"}' --destructive
# now it runs

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Patterns blocked: delete, drop, purge, wipe, kill, force=true, confirm=true. Pass --destructive to override.

Python API

from mcptoon.client import MCPClient
from mcptoon.output import toon

with MCPClient(stdio=["npx", "-y", "@modelcontextprotocol/server-fetch"]) as c:
    tools = c.list_tools()
    print(toon(tools))  # compact TOON
    result = c.call_tool("fetch", {"url": "https://example.com"})
    print(toon(result))

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Architecture

src/mcptoon/
├── cli.py        # CLI entry + arg parsing
├── client.py     # MCPClient — stdio + HTTP transport
├── router.py     # Tool routing, safety checks
├── output.py     # TOON / JSON / compact rendering
├── cache.py      # Schema cache (5-min TTL)
└── usage.py      # Local usage tracking

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1,700 lines. Zero third-party imports. I have a personal grudge against dependencies for something this simple.

vs other MCP clients

mcptoon mcp-cli raw MCP SDK Token savings 97% manifest, 56% results 0% 0% All agents yes Claude only varies Dependencies 0 5-20 3-8 Install size 50KB 50MB+ 10MB Platforms Win+Mac+Linux Linux/Mac varies

GitHub: activeing123/mcptoon
PyPI: mcptoon
License: Apache 2.0 | Dependencies: 0 | Tests: 98 passing in 0.09s

Repo is here. Issues and feedback welcome — I’m especially curious if anyone has measured MCP token overhead in production and has different numbers than mine.\n\n—\n\n> If this was useful, drop a star on GitHub — it helps others find it too.\n