by activeing123
MCP tool lists eat 71,929 tokens at 255 tools — more than half a 128K window before you ask anything. mcptoon reads the same tools back at 581 (-99.2%, measured). 128KB CLI, zero deps. Compute your own: activeing123.github.io/mcptoon/tools/token-tax
# Add to your Claude Code skills
git clone https://github.com/activeing123/mcptoonLast scanned: 8/12/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-08-12T05:37:21.978Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how mcptoon compares with popular alternatives.
mcptoon is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by activeing123. MCP tool lists eat 71,929 tokens at 255 tools — more than half a 128K window before you ask anything. mcptoon reads the same tools back at 581 (-99.2%, measured). 128KB CLI, zero deps. Compute your own: activeing123.github.io/mcptoon/tools/token-tax. It has 195 GitHub stars.
Yes. mcptoon passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/activeing123/mcptoon" and add it to your Claude Code skills directory (see the Installation section above).
mcptoon is primarily written in HTML. It is open-source under activeing123 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh mcptoon against similar tools.
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Add 1,000 MCP tools locally — your token context never feels it.
mcptoon is a 128KB CLI that keeps MCP tool schemas out of your agent's context.
Tool discovery drops 71,929 → 581 tokens at 255 tools (−99.2%, measured); call
results shrink another ~34% with --toon. One command per server, zero config,
and every agent on your machine shares the same toolkit.
👉 中文 · Developer docs · Issues
pip install mcptoon
# Add any MCP server — one command:
mcptoon add fetch --stdio npx -y @modelcontextprotocol/server-fetch
# What your agent actually reads (names only — 581 tokens, not 71,929):
mcptoon manifest
Your tools stay yours. mcptoon bundles nothing — it's a remote control, not a runtime. The MCP servers you want, you install yourself, one command each (npm/pip/a URL). Delete mcptoon someday? Your MCP servers keep running on their own — not one goes missing.
Mcptoon is the native decoupling layer for MCP tools. It fixes the twin pain of MCP tool listings eating tokens and every AI agent re-configuring tools on its own. Zero config, out of the box: it auto-scans and unifies the MCP tools of every agent on this machine — Claude Code, Cursor, Codex, scripts, CI — shares tool instances globally, and slashes token overhead.
Those numbers are ours, but "loading every tool schema into context is expensive" is not a claim only we make:
We're not the only ones who measured this. mcptoon is the one you can use today, covering every agent at once.
pip install mcptoon # pure stdlib, 128KB, zero dependencies
# Add any MCP server — one command:
mcptoon add fetch --stdio npx -y @modelcontextprotocol/server-fetch
# See every tool available (names-only by default; 255 tools cost 581 tokens):
mcptoon manifest
# Call a tool (JSON output by default; add --toon to save more):
mcptoon call fetch fetch '{"url":"https://example.com"}'
Claude Code user? Skip the terminal entirely:
/plugin marketplace add activeing123/mcptoon
The plugin auto-installs the CLI (SessionStart hook), wires the mcptoon serve bridge via .mcp.json, and ships a skill that teaches the agent when
to compress. /mcptoon-setup is the manual fallback.
Or let mcptoon auto-discover servers already on your machine:
mcptoon quickstart # discover + configure + list tools — one command
That's it. No hand-written JSON config. No MCP protocol debugging. No polluted context window. The wheel is 128KB with zero dependencies, and mcptoon itself needs no API key and phones nothing home — $0 in service fees, everything runs on your machine.
Every MCP agent (Claude Code, Cursor, Codex, …) stuffs every tool's full schema into your context window before doing any work:
50 tools → 14,113 tokens of schema → a 128K context: 11% gone
255 tools → 71,929 tokens of schema → a 128K context: 56% gone
So you unload servers you aren't using and reload them when you are. Back and forth. Adding one new server still means hand-writing a JSON config — one missing comma and everything breaks.
mcptoon fixes this. Your MCP servers stay configured, but their schemas never
enter the agent's context by default. The agent just runs mcptoon commands, and
only the compact result you asked for enters context — the name index weighs 581
tokens (114 for 50 tools, −99.2%).
Without mcptoon: 255 tools → 71,929 tokens, over half the window
With mcptoon: 255 tools → 581 tokens. 99.2% saved.
Both rows are measured configurations, not one number scaled up and down (tiktoken
cl100k_base, assets/benchmark_tiktoken.json). Your mix will differ —
compute your own numbers in the browser,
30 seconds, nothing uploaded.
Token-heavy tool context is no longer a niche complaint — it is now an official engineering problem, and the same answer keeps appearing on every roadmap:
| The fix | Where it runs | The catch |
|---|---|---|
| Tool Search Tool / PTC | Claude-platform betas | tool results still enter context token by token on every other agent |
| MuleSoft gateway | enterprise gateway | behind MuleSoft; MCP spec one generation behind |
| mcptoon | any agent that can run a shell command | none — 128KB, no key, no proxy, MCP 2026-07-28 GA |
The direction is settled. mcptoon is the version of this answer you can run today, on every agent at once — the results-side discipline without the platform or gateway toll.
# Install from npm (most MCP servers live here):
mcptoon install brave-search --npm @modelcontextprotocol/server-brave-search
# Install from pip:
mcptoon install my-tool --pip mcp-my-tool
# HTTP/SSE servers:
mcptoon install remote-api --url https://example.com/mcp
# List installed:
mcptoon install --list
# Uninstall:
mcptoon install --remove brave-search
mcptoon connects, discovers tools, generates the handler, registers it. No restart needed. Each install adds 0 KB to mcptoon itself — the CLI stays 128KB with zero dependencies, because servers are external processes your machine runs directly, not code bundled into mcptoon. Four steps, one command, no agent restart.
Any MCP server works:
mcptoon add my-server --stdio npx -y @any/mcp-package
mcptoon manifest # usable immediately
Teach your agent to use mcptoon through the open agent-skills ecosystem — the skill is picked up by Claude Code, Cursor, Codex, Cline, Windsurf and 75 more:
npx skills add https://github.com/activeing123/mcptoon --skill mcptoon
Don't want to hand-edit configs? ToonDeck is a local console for mcptoon: every MCP server and tool in one place with a real health check, one skill folder synced to all your agents, agent launching with live logs, and API keys stored in your OS keychain — never in a plaintext file.
pip install toondeck # ships the web UI inside the wheel — no node, no build
Pre-alpha; free (Apache-2.0). ToonDeck drives the engine — mcptoon stays the single source of truth underneath.
mcptoon is a CLI. If your agent can run a shell command, it can use mcptoon. No plugins, no SDK, no per-agent setup.
| Agent | How |
|---|---|
| Claude Code | put mcptoon commands in SKILL.md |
| Codex (OpenAI) | add mcptoon to AGENTS.md |
| Cursor | add mcptoon to .cursorrules |
| OpenCode | use mcptoon in custom commands |
| Any agent | can run shell commands → can call mcptoon |
Configure once in ~/.mcptoon/config.json; every agent that can run shell commands
shares the same servers and tools. GUI agents that can't? mcptoon sync writes native
JSON into each one's own location.
export MCPTOON_AGENT_TYPE=claude # call results a