ATR: Automation for Token Reduction

ATR: Automation for Token Reduction

ATR makes your AI use fewer tokens when it works with tools. Fewer tokens means lower bills and fewer "usage limit reached" messages.

It works with Claude Code, Codex, Claude Desktop, Cursor, Windsurf, claude.ai and ChatGPT. It's free and runs on your own computer.

The problem

When an AI uses tools (GitHub, a browser, a database, your test runner), two things waste tokens:

1. Every tool's instructions are sent on every message, even tools the AI never uses. Ten MCP servers can mean 10,000+ tokens before you've typed anything.
2. Tool results are huge. A list of GitHub issues, a web page or a test run can be tens of thousands of tokens, and all of it goes into the chat.

What ATR does

ATR sits between your AI app and your tools:

• It hides the tool list. The AI sees a few small ATR tools and searches for the tool it needs when it needs it.
• It shrinks results. Before the AI sees a result, ATR removes the noise and keeps what matters. A failing test run keeps the failures and drops the passing tests. A web page keeps its text and links and drops the layout.
• Nothing is lost. When ATR cuts something, it keeps the full result, and the AI can ask for any part of it.

  Your AI app (Claude Code, Codex, Claude Desktop, …)
          │  sees a few small ATR tools instead of every tool
          ▼
        ATR  ← finds the right tool, runs it, shrinks the result
          │
          ▼
  Your MCP servers (GitHub, Playwright, Postgres, Slack, …)

Get started

You need Python 3.11 or newer.

pip install atr-mcp
atr setup

atr setup finds the AI apps on your computer, shows you what it will change, and asks first. Then it:

1. moves your MCP servers behind ATR, and
2. adds ATR to each app.

Restart your apps and you're done.

Every file atr setup changes also gets a backup copy (.atr-backup).

Just want to try it?

The demo connects a fake server with 50 tools (GitHub, Slack, logs, a database). Nothing leaves your computer.

claude mcp add atr -- atr serve --demo        # Claude Code
codex mcp add atr -- atr serve --demo         # Codex

Ask your AI to "list the open GitHub issues", then "show ATR stats" to see how many tokens it saved.

How much it saves

Live tests

We gave AI agents the same browser task with and without ATR: open Hacker News, click "new", then open a GitHub issues page. Each setup ran 3 times, and every answer was correct.

Codex is mixed: with ATR it sends about half as many full-price tokens, but more tokens overall.

Single results

What the AI receives, in tokens, measured with python -m benchmarks.bench_atr:

In each case, the parts that matter are kept: errors, failing tests, changed code lines, page text and links.

When ATR helps, and when it doesn't

• Helps most: you use MCP servers that return big results, like GitHub, browsers (Playwright), databases, logs or web pages.
• Helps a little: plain coding in Claude Code or Codex with no MCP servers. Those apps already trim their own commands, so in our test ATR made no difference there.
• Can't help: your chat history and the AI's own replies. An MCP server only sees tool calls, never the conversation.

ATR's tools

The AI uses these. You don't need to call them yourself.

For your MCP servers

For coding

Safety

• Secrets are never read, searched or shown, including .env files, private keys, cloud credentials, and files named like api_keys.json. This also applies to tools behind ATR and to atr_diff.
• Reading and changing are separate. Tools that only read go through atr_read. Anything that changes something goes through atr_call, so your app can ask you before it runs. atr_run (shell commands) always asks.
• Files stay in allowed folders. File tools only see the folders listed in your settings.

Using ATR in claude.ai or ChatGPT

These apps can only reach servers on the internet, so ATR needs a public link.

pip install "atr-mcp[http]"
atr serve --http                               # prints a link with a secret in it
tailscale funnel --bg 8789                     # a permanent public https link (or: cloudflared tunnel --url http://127.0.0.1:8789)

Add https://<your-link>/mcp/<secret> as a connector:

• claude.ai: Settings → Connectors → Add custom connector (no sign-in)
• ChatGPT: Settings → Apps & Connectors → developer mode → create a connector (no sign-in)

Keep the link private. Anyone who has it can use your tools. File reading and shell commands are off over the internet unless you turn them on with --read-files and --allow-run. To change the secret, delete ~/.atr/http_token.

Settings

atr setup writes these for you. You can also edit ~/.atr/config.json yourself:

{
  "servers": {                          // the MCP servers ATR runs for you
    "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"] }
  },
  "budget": 1500,                       // most tokens one result may use
  "level": 2,                           // 1 = keep almost everything, 3 = shrink hard
  "pin": ["github__create_issue"],      // tools to always show (skips the search step)
  "read_only": ["github__list_*"],      // tools that only read, if a server doesn't say so itself
  "read_roots": ["~"]                   // folders the file tools may open
}

More

No MCP at all? atr compress shrinks a file or your clipboard before you paste it into any chat:

atr compress dump.json --copy
pbpaste | atr compress --copy

Set up by hand: atr init writes the settings file, and atr snippet prints the entry to add to your app's MCP config.

"No module named 'atr'" on macOS: run chflags nohidden .venv*/lib/python*/site-packages/*.pth, or use the entry from atr snippet.

Limits:

• Online servers that need a sign-in (OAuth) can't go behind ATR yet, so atr setup leaves them in your app.
• ATR lowers what each message costs. It doesn't change your plan's limits.

For developers

git clone https://github.com/JacobBhatt12/atr && cd atr
pip install -e ".[dev]"
python -m pytest atr/tests -c /dev/null       # 44 tests
python -m benchmarks.bench_atr                # the savings table above

Next up: remembering repeated tool calls so the same result isn't sent twice, sign-in support for online servers, and a small local model that summarizes long text when cutting would lose meaning.

License: MIT