# AI Footprint

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

- name: io.github.nickjlamb/ai-footprint
- version: 1.0.4
- connection class: R0 (autonomous)
- trust: 97/100
- quality: 91/100 (strong)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.github.nickjlamb%2Fai-footprint
- tools: https://api.protogrid.dev/v1/servers/io.github.nickjlamb%2Fai-footprint/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 100
- liveness: 100
- freshness: 100
- provenance: 90
- drivers: +repository +namespace-matches-repo +website +reachable +uptime-100% +updated-7d-ago
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 91/100 (strong)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 100 (weight 30)

- pass `tools.descriptions`: every tool has a description
- pass `tools.description_length`: descriptions are concise
- pass `tools.schemas`: every tool has an object input schema
- pass `tools.annotations`: 4 of 4 tools declare readOnlyHint or destructiveHint
- pass `tools.directory_hints`: every tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
- pass `tools.token_cost`: about 891 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 100 (weight 15)

- pass `stability.changes`: 4 tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 4, about 891 tokens to load them all
- tool set hash: 62a879a5b4306ee6a3b4cfd8e801e424
- tools last changed: 2026-10-13T20:37:25.986Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.github.nickjlamb/ai-footprint.svg)](https://protogrid.dev/servers/io.github.nickjlamb/ai-footprint)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-13 `estimate_session_footprint` changed (annotations)
- 2026-10-13 `compare_water_use` changed (annotations)
- 2026-10-13 `get_methodology` changed (annotations)
- 2026-10-13 `estimate_ai_footprint` changed (annotations)

Full history: https://api.protogrid.dev/v1/servers/io.github.nickjlamb%2Fai-footprint/changes

## Remotes

- https://footprint.pharmatools.ai/mcp (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## Tools (4)

- `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_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,
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.
- `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:
'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.
- `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.