AI Footprint
Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources.
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质量与安全性
基于对工具定义和协议合规性的自动分析。
上下文开销
这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。
安装
一键安装
将以下内容添加到你的 `claude_desktop_config.json` 文件中：
{
"mcpServers": {
"ai-footprint": {
"url": "https://prompt-tightener-production.up.railway.app/mcp"
}
}
}远程端点
https://prompt-tightener-production.up.railway.app/mcpstreamable-http它能做什么
工具清单
工具（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.
输入模式
{
"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.
输入模式
{
"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.
输入模式
{
"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.
输入模式
{
"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"
}社区
证据