AI Footprint

Last 30 days

Uptime

100%

Response time

218 ms typical, 282 ms slowest 5%

Last check

56 min ago

Next check

in 5 h

How to call it

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "ai-footprint": {
      "type": "http",
      "url": "https://footprint.pharmatools.ai/mcp"
    }
  }
}

4 tools

• 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

• 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

• 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 r

Security scan

• No findings. We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.

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