Keep the signal. Cut the noise. Local CLI + MCP — fold verbose tool output for Claude Code, Codex, Cursor & Desktop.. It is open-source on GitHub.

Is tokencut free to use?

tokencut is open-source under the MIT license, so it is free to use.

What category does tokencut belong to?

tokencut is listed under mcp-servers in the Claudeers registry of Claude-compatible tools.

tokencut — MCP Servers for Claude

Platforms	cli, api, desktop
Operating systems	—
AI compatibility	claude
License	MIT
Pricing	open-source
Language	Python

TokenCut: keep the signal, cut the noise. Authored fixtures cut docker 18,360→107, cargo 3,795→185, pytest 1,562→174.

Keep the signal. Cut the noise.
Local CLI + MCP that folds verbose tool output for Claude Code, Codex, Cursor, and Claude Desktop — recoverable by reference. Optional macOS pet.

Install · Demo · Features · Connect · Pet · Guide

Harnesses and tools: Claude, Codex, Cursor, Desktop, pytest, docker, cargo, go, eslint, mypy

Install

Requires Python 3.11+ and uv.

uv tool install 'git+https://github.com/00200200/tokencut.git'
tokencut demo

Install from this GitHub repo. The PyPI package named tokencut is a different project.

verbose tool text  →  keep the failure  →  recover the rest by reference

See it cut

tokencut demo is offline — no model calls. Failures stay; originals recover exactly.

Measured savings: docker 99.4%, go 98%, terraform 96%, cargo 95.1%, kubectl 92.5%

Terminal showing tokencut demo measured fixture results

Fixture	Tokens
`docker build` BuildKit	18,360 → 107 (99.4%)
`go test` + goroutine dump	3,281 → 64 (98.0%)
`terraform plan` refresh/read	5,885 → 236 (96.0%)
`cargo test` + backtrace	3,795 → 185 (95.1%)
pytest noisy (xdist + I/O)	5,111 → 348 (93.2%)
`kubectl describe` pod	8,049 → 601 (92.5%)
pytest recovery demo	1,562 → 174 (88.9%)
`vitest` / `eslint` / `tsc`	up to 98.8% / 79%

Local o200k_base estimate — not billing, quality, or subscription-limit claims. Full matrix: tokencut demo --json.

tokencut run -- pytest -v
tokencut run -- docker build -t app .
tokencut run -- cargo test
tokencut run -- go test ./...
tokencut run -- kubectl describe pod api-7d8f9c-xk2m9
tokencut run -- terraform plan
tokencut run -- npx eslint . --format codeframe

What you get

Cut noise, keep the failure. Specialized filters for pytest, Docker, cargo, go, vitest, eslint, tsc, mypy, pyright, kubectl, terraform, uv sync/uv add, git diff, ruff…
Session dedup + spill. Same run output or identical cat / MCP tokencut_read view within ~15 minutes → short cache ref. Payloads over ~20 KiB → file + preview (TOKENCUT_SPILL_BYTES).
Recover by reference. Omitted text stays in a local CCR cache: tokencut retrieve tc_…
Measure it. tokencut gain / MCP tokencut_gain — per-tool-family savings and passthrough candidates (local estimates, not account quotas).
Desktop-ready. MCP for Claude Code / Codex / Cursor / Claude Desktop; Prepare-for-chat clipboard flow; optional macOS pet.

tokencut gain                 # summary + by tool family + passthrough tips
tokencut gain --history       # same tables + recent Raw→Compact / Saved rows
tokencut gain --passthrough   # near-zero cuts only (specializer candidates)
tokencut prepare --file draft.txt

Details and tradeoffs →

How it fits

TokenCut is not a chat interceptor. It sits on the tool path (CLI wrapper, MCP, Prepare-for-chat) so agents still see failures — just without the noise.

Approach	What TokenCut does instead
Blind head/tail truncation	Specialized cutters keep the failure signal; rest recovers via `tokencut retrieve`
Rewrite the whole chat stream	MCP + `prepare` only — Desktop cannot rewrite model turns
Opaque “saved tokens” badges	`tokencut gain` shows local Raw→Compact by tool family (not billing quotas)

Same CCR idea as peers (compress → cache → retrieve). Differentiation is specialized cutters, session dedup, spill-to-file, and Desktop/MCP install paths — not a claim that we beat RTK/snip/headroom on every workload.

Connect your agent

Desktop & Coding profiles: 9–11 essential tools instead of 16 — about 36–38% smaller tool schemas in local o200k_base measurements (3,414 → 2,183 / 2,102). Not a per-turn usage guarantee.

# One-command installer for Codex & Claude Desktop
tokencut install --codex            # configures ~/.codex/config.toml
tokencut install --claude-desktop   # configures Claude Desktop MCP
tokencut install --mcpb             # Extension manifest for one-click packaging
tokencut install --all              # configures all at once

For Claude Code CLI:

claude mcp add --scope user tokencut -- tokencut mcp --profile coding

Manual MCP configuration for Claude Desktop, Cursor, Codex, Windsurf:

{
  "mcpServers": {
    "tokencut": {
      "command": "/absolute/path/to/tokencut",
      "args": ["mcp", "--profile", "desktop"]
    }
  }
}

Path: command -v tokencut. Use --profile full for every tool, --profile coding for core terminal tools, or --profile desktop for chat apps. Client setup →

# Prepare messy logs or stack traces with prompt-cache prefix stabilization:
tokencut prepare --desktop -f error.log

# Initialize or optimize lean, cache-aligned instructions (CLAUDE.md / AGENTS.md):
tokencut rules --init --client claude        # writes lean CLAUDE.md (~120 tokens)
tokencut rules --init --client codex         # writes lean AGENTS.md (~120 tokens)
tokencut rules --optimize --write -f CLAUDE.md # strips filler, aligns prompt caching

Desktop companion

Mint robot on your Mac — draggable pet or menu-bar mode. Local measurements, task memory, optional account-limit readings. English UI. No extra AI calls.

TokenCut macOS pet with example five-hour balances: Codex 68% left, Claude 42% left.