Skip to main content
Glama
evanwang810

thirsty-tokens

by evanwang810
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

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).

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.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • claudeusage-mcp

    Orellius-Archive
    A
    license
    A
    quality
    Not graded
    maintenance
    Provides real-time visibility into Claude Pro and Max subscription usage limits directly within Claude Code by utilizing local OAuth tokens. It enables users to monitor session and weekly usage across different models and receive alerts regarding rate-limiting status.
    4
    -

  • AI Impact MCP

    anchetadev
    F
    license
    A
    quality
    D
    maintenance
    Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
    9
    -

  • claude-spend

    mpalermiti
    A
    license
    Not graded
    quality
    B
    maintenance
    Provides tools for Claude to query local Claude Code token usage and cost data, enabling spend analysis and insights through natural language.
    11 npm
    1
    MIT