AI Footprint
Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"ai-footprint": {
"url": "https://prompt-tightener-production.up.railway.app/mcp"
}
}
}Remote endpoints
https://prompt-tightener-production.up.railway.app/mcpstreamable-httpWhat it can do
Tool inventory
Tools (4)
🟢estimate_ai_footprint(queries_per_day, usage, boundary, days)
Estimate the water (mL) and energy (Wh) used by chat-style AI prompts (ChatGPT, Claude, Gemini etc.). queries_per_day: prompts per day. usage: 'simple' (text chat, ~0.34 Wh/prompt), 'mixed' (~1 Wh), 'intensive' (image generation / deep reasoning, ~3 Wh). boundary: 'onsite' (data-centre cooling only, as vendors report) or 'comprehensive' (adds water used to generate the electricity – the wider boundary most researchers use). days: number of days to total over (e.g. 250 workdays, 365). Returns per-prompt, per-day and total figures with everyday equivalents, assumptions and a source link. For agentic/coding sessions use estimate_session_footprint instead.
Input Schema
{
"type": "object",
"properties": {
"queries_per_day": {
"title": "Queries Per Day",
"type": "number"
},
"usage": {
"default": "simple",
"enum": [
"simple",
"mixed",
"intensive"
],
"title": "Usage",
"type": "string"
},
"boundary": {
"default": "comprehensive",
"enum": [
"onsite",
"comprehensive"
],
"title": "Boundary",
"type": "string"
},
"days": {
"default": 1,
"title": "Days",
"type": "integer"
}
},
"required": [
"queries_per_day"
],
"title": "estimate_ai_footprintArguments"
}🟢get_methodology
Return the per-prompt energy and water figures, accounting boundaries, sources, limitations, a citation line, and why published AI water estimates differ by ~100x (0.3 mL to 50 mL per prompt). Call this before explaining or citing any AI water figure.
Input Schema
{
"type": "object",
"properties": {},
"title": "get_methodologyArguments"
}🟢compare_water_use(water_ml)
Convert a volume of water (mL) into everyday comparisons: teaspoons, 500 mL bottles, shower time, and lifecycle items (A4 sheet, almond, cup of tea, beef burger), plus the number of typical AI text prompts it equals. Includes the boundary caveat that lifecycle footprints are not like-for-like.
Input Schema
{
"type": "object",
"properties": {
"water_ml": {
"title": "Water Ml",
"type": "number"
}
},
"required": [
"water_ml"
],
"title": "compare_water_useArguments"
}🟢estimate_session_footprint(input_tokens, output_tokens, cached_input_tokens, model_class, boundary)
Estimate the water and energy of an AI session (e.g. an agent or coding-assistant run) from its token usage. Pass the SUM across all model calls: input_tokens (uncached prompt tokens), output_tokens (including any reasoning/thinking tokens) and cached_input_tokens (prompt-cache reads). model_class: 'small' (Haiku/mini/Flash-class), 'frontier' or 'reasoning'. Returns a central estimate and a 0.3x–3x range – this is an order-of-magnitude estimate calibrated to published per-prompt figures, not a measurement.
Input Schema
{
"type": "object",
"properties": {
"input_tokens": {
"title": "Input Tokens",
"type": "integer"
},
"output_tokens": {
"title": "Output Tokens",
"type": "integer"
},
"cached_input_tokens": {
"default": 0,
"title": "Cached Input Tokens",
"type": "integer"
},
"model_class": {
"default": "frontier",
"enum": [
"small",
"frontier",
"reasoning"
],
"title": "Model Class",
"type": "string"
},
"boundary": {
"default": "comprehensive",
"enum": [
"onsite",
"comprehensive"
],
"title": "Boundary",
"type": "string"
}
},
"required": [
"input_tokens",
"output_tokens"
],
"title": "estimate_session_footprintArguments"
}Community
Evidence