mcpField
T
Tare

Tare

Local
tare.priberry.comData & Analytics1Updated 2d ago

About Tare

Tare analyzes local coding-agent logs to estimate token costs and identify avoidable usage, then provides cache-aware fixes and receipts. It is for developers tracking agent usage; it runs locally, uploads nothing, and does not call a model.

Key features

  1. 01Local agent-log cost analysis
  2. 02Token usage breakdowns
  3. 03Cache-aware output trimming
  4. 04Cache status and countdown
  5. 05Cost-per-commit receipts
  6. 06MCP tools for usage reports

Use cases

  1. 01Reviewing coding-agent costs over time
  2. 02Finding repeated or costly context output
  3. 03Trimming tool output without rewriting history
  4. 04Comparing cost per commit before and after fixes

MCP support

Tare can run as an MCP server over stdio, exposing `tare_wrapped`, `tare_receipt`, and `tare_cache`. Its setup command can add Tare to detected AI coding apps.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"priberry-data-tarelab": {
"command": "npx",
"args": [
"-y",
"tarelab"
]
}
}
}
Run commandnpx -y tarelab

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmtarelab0.2.0

Tools

3

Saved tool list · updated 1d ago

ParameterTypeRequiredDescription
daysnumberNoWindow in days; 0 for all time. Default 30.

Details

From the project README

tare

Same work, fewer tokens, with the receipt.

Your coding agent keeps a log of every API call it makes. Tare reads those logs, prices every token, and shows where the money went: Claude Code, Codex CLI, Gemini CLI, and Cline, Roo Code and Kilo Code in VS Code, Cursor, Windsurf and the other VS Code-based editors. For Codex on a ChatGPT plan it also shows how close you came to the plan's usage limits. Then it cuts the waste without breaking the prompt cache, and proves each saving on your own work. It runs locally, uploads nothing and never calls a model.

npx tarelab
  YOUR AGENT, WRAPPED  · last 30 days · 254 sessions · 79,905 API calls

  $6,720 API-equivalent spend
  $623 of it was avoidable (9%)

  Where it went
  Reading cached context     ██████████████████████ $3,809
  Writing to the cache       ████████████ $2,072
  Output (thinking incl.)    █████ $838

  The highlights
  › ~82k tokens load before you type a word.
  › Coffee-break tax: $722 re-caching after 636 pauses.
  › Sub-agents spent $2,734 (41%).

A real month of Priberry's own Claude Code use.

Commands

CommandWhat it does
tareWrapped for the last 30 days. --days N, --all, --json, --logs <dir>
tare cardA 1200×630 shareable card (SVG, with a PNG download) and post text. Nothing is uploaded
tare fixInstalls the output trim and the cache status line. --dry-run, --undo, --scope project
tare receiptCost per commit before and after tare fix, with a 90% range and a finish-rate check
tare statusWhat is installed, and how much output the trim has cut so far
tare setupAdds Tare to every AI coding app on this machine. --dry-run, --undo
tare mcpMCP server over stdio: tare_wrapped, tare_receipt, tare_cache

How the numbers work

  • API-equivalent. Every call is priced at Anthropic's API list prices: fresh input, cache writes (1.25× input for the 5-minute cache, 2× for the 1-hour cache), cache reads and output. On a subscription you pay a flat fee; this is what the same work would cost on the API.
  • Carry cost. A tool's output is written to the cache once and read back on every later call until the conversation is compacted. Tare charges each output for the whole time it stays in context.
  • Cache rebuilds. When most of a prompt is written to the cache again instead of read from it, Tare says why: an idle pause past the cache's lifetime, a model switch, a compaction, or a break.
  • Repeats. A tool call that returned byte-for-byte what the agent already had.

The fixes

Tare cuts waste where it enters the context and never rewrites what's already there, because a rewritten history breaks the prompt cache and costs more than it saves.

  • Output trim (a PostToolUse hook on Bash): output over ~2,500 tokens is cut to the first 40 lines, every line that looks like a failure (with context) and the last 60 lines. The full output is saved in ~/.tare/out/ and the agent is told where.
  • Cache countdown (the status line): how long until the cache goes cold, and what the next message costs warm versus cold. Installed only if you don't already have a status line.

Settings are backed up to ~/.tare/backups/ first, and tare fix --undo removes only Tare's entries.

The receipt

tare receipt compares cost per commit in sessions before and after the fixes, with a bootstrap 90% interval, and checks that as many sessions still reach a commit. With fewer than 8 finished sessions on either side it prints "not enough data" and no number.

In your agent

One command adds Tare to every AI coding app it finds: Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, Codex, Gemini CLI, Kiro, opencode, Amazon Q, LM Studio, and Cline, Roo Code and Kilo Code. The entry is pinned to the exact version, each file is backed up first, and --undo removes it.

npx tarelab setup

Or one app at a time:

  • Claude Code plugin: /plugin marketplace add priberry-data/tarelab, then /plugin install tare@priberry.
  • Gemini CLI extension: gemini extensions install https://github.com/priberry-data/tarelab
  • Skill (Claude Code or Codex): copy skills/tare into ~/.claude/skills/ or ~/.codex/skills/.
  • Any MCP client: {"command": "npx", "args": ["-y", "[email protected]", "mcp"]}. Then ask "what did my agent cost this month?"

Limits

Tare reads the logs that agents keep on your machine. Cursor's own agent, Windsurf's Cascade, Copilot and the browser app builders (Lovable, Bolt, v0, Replit) keep usage on their servers, so Tare can't see them yet; opencode is next. Cline-family costs are the ones the extension itself recorded. The fix and receipt commands work on Claude Code, where the hooks exist. Token counts for tool output are estimated from text length (the API reports totals per call, not per block). Prices are as of 2026-10-06 for Anthropic and 2026-11-08 for OpenAI and Google, and live in src/prices.mjs.

Apache-2.0 · Built by Priberry, who also make Crumple.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.