thirsty-tokens

How much data-center water has your Claude Code habit evaporated? This reads your own local transcripts (~/.claude/projects) and estimates it, with real-world comparisons. Nothing leaves your machine.

Four ways to use it: a CLI, a live desktop widget, an MCP server, and a browser calculator (no install).

• Live web calculator: https://evanwang810.github.io/thirsty-tokens/

• Download the desktop app (Windows .exe): https://github.com/evanwang810/thirsty-tokens/releases/latest

Install

git clone https://github.com/evanwang810/thirsty-tokens
cd thirsty-tokens
pip install -e .

Needs Python 3.10+. The only dependency is the MCP SDK (for the server); the CLI and widget are stdlib-only.

Related MCP server: AI Impact MCP

CLI

thirsty --open          # summary + HTML report, opened in the browser
thirsty --since 7       # last week only
thirsty --no-report     # just the terminal summary

Knobs for the skeptical: --throughput, --water-intensity, --gpu-watts.

Live desktop widget

A little always-on-top pill that re-reads your transcripts every few seconds and watches the water climb as you work. Drag it anywhere; right-click for the menu.

thirsty-app            # full pywebview dashboard
thirsty-app --widget   # start as the floating pywebview widget
thirsty-widget         # widget-first shortcut

MCP server

Built on the official MCP SDK. Wire it into Claude Code:

claude mcp add thirsty --scope user -- python -m thirsty.server

Restart Claude Code, then claude mcp list should show thirsty ... Connected.

Tools: water_summary, water_widget (returns a compact HTML widget as an embedded resource), water_breakdown (by model and project). Resources: thirsty://report, thirsty://widget. There's also a /water-usage slash command that runs the CLI and summarizes it.

Website

docs/index.html is a self-contained calculator hosted on GitHub Pages. Type in a token count, get the water. It links back here for the auto-tracking tools.

The model

Water = electricity x water intensity.

• Electricity: a B200 (~1 kW) x1.8 node overhead x1.2 PUE, serving ~1,500 output-equivalent tokens per GPU-second. Lands near 0.4 Wh per 1,000 tokens.

• Water intensity: 3.0 L/kWh (on-site cooling ~1.8 + off-site power ~1.2).

• Token weighting: output counts full, prefill x0.2, cache reads x0.02.

Order-of-magnitude only, likely off by 3 to 5x either way. It depends on model size, batch depth, data-center location, and grid mix. Treat it as a gut-check, not a bill.

MIT licensed.