MCP server

AI Footprint

io.github.nickjlamb/ai-footprint

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

4

Token cost

About 911 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.ai-footprint]
url = "https://footprint.pharmatools.ai/mcp"

Gemini CLI

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

Cursor

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

VS Code

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

Antigravity

agy mcp add ai-footprint https://footprint.pharmatools.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ai-footprint","server_url":"https://footprint.pharmatools.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ai-footprint","server_url":"https://footprint.pharmatools.ai/mcp"}

Tools

• compare_water_use ~143 tokens
  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_ai_footprint ~332 tokens
  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
• estimate_session_footprint ~323 tokens
  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:
• get_methodology ~113 tokens
  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.

Browse

• Claude tools
• GitHub tools
• Invoicing and accounting
• GitHub and code hosting
• Image generation
• Calling other AI models

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