AI Footprint
Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources.
사용해야 할까요
품질 및 안전성
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"ai-footprint": {
"url": "https://prompt-tightener-production.up.railway.app/mcp"
}
}
}원격 엔드포인트
https://prompt-tightener-production.up.railway.app/mcpstreamable-http할 수 있는 일
도구 목록
도구 (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.
입력 스키마
{
"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.
입력 스키마
{
"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.
입력 스키마
{
"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.
입력 스키마
{
"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"
}커뮤니티
증거