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AI Footprint

io.github.nickjlamb/ai-footprint · v1.0.4

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

MCPremoteno credentialsAI & LLMMessagingImages, video & audio

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Last check

Reachable, 4 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 93/100

What it does: 4 tools · 4 read, 0 write · AI & LLM, Messaging, Images, video & audio

Test call get_methodology: worked (1,001 ms)

How the rating is made
  • Answers without login: +30
  • Speed of first answer: +10
  • Lists tools: +10
  • Tool descriptions: +15
  • Parameter descriptions: +3
  • Read/write annotations: +5
  • Safe test call: +20
  • More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL
https://footprint.pharmatools.ai/mcp

Tools

4 tools listed at last check
  • estimate_ai_footprint 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
  • get_methodology Return the per-prompt energy and water figures, accounting boundaries, sources, limitations, a citation line, and the reasons published AI water estimates differ by ~100x (0.3 mL to 50 mL per prompt). Use it to explain or check a published AI water or energy figure.
  • compare_water_use 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.
  • estimate_session_footprint 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:

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