contextburn

Name: contextburn
Author: arsentev

@arsentevMCP server · External

Run efficiency for coding agents: share of paid tokens that became output, not context re-reading.

AI evaluation & benchmarksv0.2.1MITPython

Freeno charge per call

How to install

Runs on your machine from its package; the steps are below.

contextburn detail 72 over the 36 experiment runs: 168 sessions, useful work 1.44% of tokens, context re-reading 94.4%, cost-weighted 28.8% — From the README

What it does

Reads
Writes
High impact

At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Runs locally over stdio

This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).

PyPIcontextburnv0.2.1

Run

uvx contextburn

MCP client config

{
  "mcpServers": {
    "contextburn": {
      "command": "uvx",
      "args": [
        "contextburn"
      ]
    }
  }
}

README3 min readView on GitHub

contextburn: real output over 24 hours — useful work 0.18% of tokens, context re-reading 98.4%, cost-weighted useful work 6.7%, one useful token costs 555 paid tokens

contextburn reads the transcripts Claude Code already writes on your machine and tells you what share of the tokens you paid for became model output — and how much was the agent re-reading context it had already sent.

Token counters answer "how much did I spend?". This answers "how much of it was work?" — a normalised share, so it can be compared across sessions, models and ways of working.

Try it

bash

cp bin/contextburn ~/bin/contextburn && chmod +x ~/bin/contextburn   # python3 only, no dependencies
contextburn detail 24

Demo

contextburn detail 72 over the 36 experiment runs: 168 sessions, useful work 1.44% of tokens, context re-reading 94.4%, cost-weighted 28.8%

Real output over the session logs of the 36 runs behind the U-curve report — nothing else on the machine. Video with DOI: 10.5281/zenodo.22713920. The runs themselves are open: Hugging Face (DOI 10.57967/hf/10366) · Kaggle · OSF (DOI 10.17605/OSF.IO/5QTWY).

Why two numbers

Same 12 tasks, one long session versus twelve short, 3 runs each: token efficiency 1.11% vs 1.12%, no difference; cost-weighted efficiency 31.6% vs 24.6%, seven points apart

By tokens the share barely moves. Every agent step resends the accumulated context, so re-reading dominates whatever you do — it describes the agent.
Cost-weighted the share does move, because cached reads are priced far below fresh input and output. It depends on how you run sessions — it describes you.

The comparison above comes from a controlled experiment with its dataset and analysis scripts: Clear Every Third Task: A Measured U-Curve in the Context Economy of Coding Agents.

How it counts

Reads local Claude Code transcripts (~/.claude/projects/**/*.jsonl). Nothing leaves the machine — no network calls at all.
Deduplicates usage records by message id and keeps the element-wise maximum. A streaming runtime writes an early snapshot and a final record for the same call: counting both double-counts it, keeping only the first halves the output.
Weights the cost share with per-model prices kept at the top of bin/contextburn. Update them there when they change.

Commands

command	what it shows
`contextburn`	what is burning tokens right now
`contextburn detail [hours]`	run efficiency, sessions, and what specifically inflated the context
`contextburn window`	the current 5-hour subscription window
`contextburn --json`	machine-readable state (used by the menu-bar app)
`contextburn --probe <hours>`	raw JSON dump of the parsed sessions
`contextburn --efficiency [hours]`	run efficiency as JSON
`contextburn mcp`	start the MCP server

Configuration

setting	default	meaning
`CONTEXTBURN_LANG` or `~/.config/contextburn/lang`	`en`	interface language: `en` or `ru`
`CONTEXTBURN_DAY_START`	`6`	hour your day starts — the daily total resets here
`CONTEXTBURN_WARN`	`30000000`	tokens/hour that turns the menu-bar counter yellow
`CONTEXTBURN_ALARM`	`90000000`	tokens/hour that turns it red

The language file exists because the menu-bar app is launched from Finder, where environment variables never reach it: echo ru > ~/.config/contextburn/lang switches both the app and the CLI.

MCP server

Let the agent read its own run efficiency mid-session. The package ships a dependency-free MCP server (stdio) with two tools: run_efficiency returns the shares as structured data, and spend_breakdown returns the full report.

bash

claude mcp add contextburn -- uvx contextburn mcp

Or install it as a Claude Code plugin, which registers the same server:

text

/plugin marketplace add arsentev-ai/contextburn
/plugin install contextburn@contextburn

Editor extensions

VS Code-compatible editors (VSCodium, Cursor, Windsurf, Gitpod…) — Open VSX: arsentev-ai.contextburn. A status bar meter over the local CLI; source in editors/vscode.
Raycast — source in editors/raycast, Store submission pending.

app/main.swift is a small status-bar app. It polls contextburn --json once a minute and shows the current burn rate with an hourly graph; click a bar to see that hour's breakdown.

bash

swiftc -O -o ContextBurn app/main.swift

Set CONTEXTBURN_BIN=/path/to/contextburn if the CLI is not in ~/bin or the usual Homebrew paths.

Limits

Claude Code transcripts only, for now.
The cost-weighted share is only as current as the price table in bin/contextburn.

Citing

Software DOI (all versions): 10.5281/zenodo.22712985. GitHub's "Cite this repository" button gives the reference; metadata is in CITATION.cff.

Author

Evgenii Arsentev — arsentev.ai · ORCID 0000-0002-9120-7298

This project was published as tokmon on its first day and renamed to avoid confusion with unrelated tools of that name; TOKMON_* environment variables still work.