AI Footprint MCP Server

Install AI Footprint MCP server

claude mcp add --transport http ai-footprint https://footprint.pharmatools.ai/mcp

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

{
  "mcpServers": {
    "ai-footprint": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://footprint.pharmatools.ai/mcp"
      ]
    }
  }
}

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

{
  "mcpServers": {
    "ai-footprint": {
      "serverUrl": "https://footprint.pharmatools.ai/mcp"
    }
  }
}

Context tax

Connecting this server adds ~845 tokens of tool definitions to every model turn (lean, 4 tools). Measured 2026-10-19 via its hosted endpoint; o200k tokenizer, worst case for clients that load all tools up front. How we measure

Input prices as of 2026-10-06. Excludes your own prompt and output.

Tools (4)

• estimate_ai_footprint288 tok

  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 ag

• estimate_session_footprint263 tok

  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.

• compare_water_use112 tok

  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.

• get_methodology91 tok

  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.

Security signals

100/100 — no known issues found as of 2026-10-19, from the checks below. This is not a guarantee of safety; review what a server can do before connecting it.

Scan it yourself before installing with vectra-guard, the open-source agent security CLI. Scoring rules

Freshness

Active — last maintenance signal 2d ago. The newest of the signals below sets the band.

1. Last commit (default branch)

   2026-10-16 · 2d ago · GitHub

2. Latest release

   2026-10-13 · 5d ago · GitHub · v1.0.4

3. Package published

   no data · npm/PyPI

4. Registry entry updated

   2026-10-13 · 5d ago · official registry · v1.0.4