tokencut
Analyzes pull requests for token impact and generates Markdown summaries for GitHub PR review comments, helping prevent context bloat from lockfiles and fixtures.
Supports Gemini CLI with context compression and optimizes Gemini prompt caching by detecting cache-invalidating dynamic content.
Reduces token consumption for OpenAI-powered coding sessions such as ChatGPT macOS by compacting terminal output and structured payloads while preserving failures.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@tokencutCompact this terminal output and keep the full error traceback"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Install
Requires Python 3.11+ and uv (pipx works too).
uv tool install git+https://github.com/00200200/usagetrim
usagetrim demoTry without installing: uvx --from git+https://github.com/00200200/usagetrim usagetrim demo.
Add it inside your app
App | Install |
Claude Code |
|
Claude Desktop | Customize → Plugins → add the marketplace |
Codex app & CLI |
|
Cursor, Windsurf |
|
Plugins run the pinned release wheel through uvx, so they need uv on PATH. The .mcpb extension installs its own copy through Claude Desktop.
verbose tool text → keep the failure → recover the rest by referenceRelated MCP server: project-graph-mcp
See it cut
usagetrim demo is offline — no model calls. Failures stay; originals recover exactly.
Fixture | Tokens |
| 18,360 → 107 (99.4%) |
| 3,281 → 64 (98.0%) |
| 5,885 → 236 (96.0%) |
| 3,795 → 185 (95.1%) |
pytest noisy (xdist + I/O) | 5,111 → 349 (93.2%) |
| 8,049 → 601 (92.5%) |
pytest recovery demo | 1,562 → 174 (88.9%) |
| up to 98.8% / 79% |
Local o200k_base estimate — not billing, quality, or subscription-limit claims. Full matrix: usagetrim demo --json.
usagetrim run -- pytest -v
usagetrim run -- docker build -t app .
usagetrim run -- cargo test
usagetrim run -- go test ./...
usagetrim run -- kubectl describe pod api-7d8f9c-xk2m9
usagetrim run -- terraform plan
usagetrim run -- npx eslint . --format codeframeWhat you get
Compact other MCP servers, losslessly. The Claude Code hook rewrites results from other MCP servers: uniform JSON rows become TSV with the keys once,
{"result": "…"}wrappers lose their escaping, and indentation goes. Every value and type survives, and prompt-injection boundaries such as Supabase's<untrusted-data-…>stay verbatim. Measured below.Spend Haiku, not Opus, on reading. The plugin adds
scout(read-only search) andrunner(tests and builds, failures only) agents on Haiku, so the main model gets conclusions instead of files and logs.usagetrim install --cheap-exploremoves Claude Code's built-in Explore, which now inherits the main model, back to Haiku./output-style usagetrim:leantrims reply preambles and recaps.Cut noise, keep the failure. Specialized filters for pytest, Docker, cargo, go, vitest, eslint, tsc, mypy, pyright, kubectl, terraform, GitHub Actions logs (
gh run view --log-failed),uv sync/uv add, git diff, ruff…Session dedup + spill. Same
runoutput or identicalcat/ MCPusagetrim_readview within ~15 minutes → short cache ref. Payloads over ~20 KiB → file + preview (USAGETRIM_SPILL_BYTES).Recover by reference. Omitted text stays in a local CCR cache:
usagetrim retrieve tc_…Measure it.
usagetrim gain/ MCPusagetrim_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.
usagetrim gain # summary + by tool family + passthrough tips
usagetrim gain --history # same tables + recent Raw→Compact / Saved rows
usagetrim gain --passthrough # near-zero cuts only (specializer candidates)
usagetrim prepare --file draft.txtMeasured on real sessions
14 days of the maintainer's Claude Code transcripts, replayed offline through the same functions the hook runs. Token counts are local o200k_base estimates, not billing.
Tool results | Count | Tokens before → after | Cut |
All third-party MCP results | 11,058 | 7.17M → 5.85M | 18.3% |
Supabase | 6,571 | 4.97M → 4.03M | 18.9% |
Google Search Console analytics | 223 | 702k → 434k | 38.2% |
Vercel | 43 | 250k → 210k | 16.1% |
All 3,144 SQL results rewritten as TSV decoded back to the original rows (reproduce on your own transcripts: uv run python scripts/replay_mcp_transcripts.py). Browser and page-text tools return prose and were left alone. Bash output in the same sessions was mostly ad-hoc scripts, where the filters saved under 1%. Test and build logs are where the Bash filters pay off, so results depend on what your tools print.
How it fits
UsageTrim 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 UsageTrim does instead |
Blind head/tail truncation | Specialized cutters keep the failure signal; rest recovers via |
Rewrite the whole chat stream | MCP + |
Opaque “saved tokens” badges |
|
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
usagetrim install --codex # configures ~/.codex/config.toml
usagetrim install --claude-desktop # configures Claude Desktop MCP
usagetrim install --mcpb # Extension manifest for one-click packaging
usagetrim install --all # configures all at onceFor Claude Code CLI:
claude mcp add --scope user usagetrim -- usagetrim mcp --profile coding
# or, without installing first:
claude mcp add --scope user usagetrim -- uvx --from git+https://github.com/00200200/usagetrim usagetrim mcpManual MCP configuration for Claude Desktop, Cursor, Codex, Windsurf:
{
"mcpServers": {
"usagetrim": {
"command": "/absolute/path/to/usagetrim",
"args": ["mcp", "--profile", "desktop"]
}
}
}Path: command -v usagetrim. 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:
usagetrim prepare --desktop -f error.log
# Initialize or optimize lean, cache-aligned instructions (CLAUDE.md / AGENTS.md):
usagetrim rules --init --client claude # writes lean CLAUDE.md (~120 tokens)
usagetrim rules --init --client codex # writes lean AGENTS.md (~120 tokens)
usagetrim rules --optimize --write -f CLAUDE.md # strips filler, aligns prompt cachingDesktop 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.
Example balances are remaining allowance — not savings caused by UsageTrim.
Prepare for chat — paste a log, preview the cut, copy into Codex / Claude Desktop. Log prep →
macOS 13+ · ad-hoc signed, not notarized. CLI and MCP work without the pet. Build →
Roadmap
Ideas from the strongest tools in this space, adopted only with a measurement behind them:
Ranked repo map (Aider-style PageRank over the symbol graph) fitted to a token budget.
LSP-backed references and rename (Serena-style), falling back to the ast-grep index.
Lossless log templating (Drain-style) for dev-server, Docker and CI logs.
A paired A/B harness on provider-reported tokens and task pass rate, because output-size savings alone can hide extra turns.
Make it better with us
If UsageTrim earns a place in your workflow, star the repo. Lost context or a missed cut? Open an issue with a small redacted example.
Guide · Measurements · MIT
This server cannot be deployed
Maintenance
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Shared distillation cache for AI agents — every fetch ~73-89% fewer tokens via a shared cache.
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