contextburn
Run efficiency for coding agents: share of paid tokens that became output, not context re-reading.
Links
README
From the repo.
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
cp bin/contextburn ~/bin/contextburn && chmod +x ~/bin/contextburn # python3 only, no dependencies
contextburn detail 24
Demo
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
- 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.
claude mcp add contextburn -- uvx contextburn mcp
Or install it as a Claude Code plugin, which registers the same server:
/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.
Menu-bar app (macOS)
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.
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.
License
MIT — see LICENSE.
Config for your environment
Replace {MCP_ENDPOINT_URL} with this MCP’s endpoint URL (from its repo or docs above). No API key — you connect directly.
Tool
OS
Config file: ~/.cursor/mcp.json
{
"mcpServers": {
"mcp-server": {
"url": "{MCP_ENDPOINT_URL}"
}
}
}Paste into mcpServers in the config file. Restart Cursor after saving.
If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.