AI Footprint

Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources.

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Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
68%
Qualität der Benennung
85%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~861Tokens (Tool-Definitionen)
~884 BTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (0.67% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "ai-footprint": {
      "url": "https://prompt-tightener-production.up.railway.app/mcp"
    }
  }
}

Remote-Endpunkte

https://prompt-tightener-production.up.railway.app/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (4)

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🟢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.

Eingabe-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.

Eingabe-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.

Eingabe-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.

Eingabe-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"
}

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