AI Footprint
REMOTE · FOOTPRINT.PHARMATOOLS.AI
Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources.
Available components
Last scanned (2 hours ago)
Scores higher than 69% of remote servers
Unreachable for 2 days in a row. Last reached 3 Nov 2026.
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security11
- TLS not yet verified: we couldn't open a connection to check the certificate. View diagnostics → Unverified
- Authorisation not yet verified: we couldn't confirm whether this endpoint requires it. View diagnostics → Unverified
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- HSTS not yet verified: we couldn't read the response headers to check for it. View diagnostics → Unverified
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability0
- Transport check failed: declared streamable-http, but we couldn't connect to verify it. See how to fix → View diagnostics → Fail
Schema Quality & AI Usability0
- Schema not yet verified: we couldn't read the endpoint's schema, or could read only part of its tool list.Unverified
Stability & Change Management0
- Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage0
- Tool coverage not yet verified: we couldn't read the endpoint's tools, or could read only part of the list.Unverified
Tool Safety0
- Tool safety not yet verified: we couldn't read the endpoint's tools, or could read only part of the list.Unverified
Capabilities0
- Capabilities not yet verified: we couldn't read the endpoint's capabilities.Unverified
Unverified: 5 categories
Categories scored 0 because we could not verify them: authentication we do not have, an unreachable endpoint, or not enough scan history. We only credit what we can confirm.
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 4 Nov 26 0
- Endpoint reachability: reachable → unreachable ▼ security
- HSTS header: fail → unverified ▼ security
- Authorization: partial → unverified ▼ security
- Stability: 0.70 → unverified ▼ security
- Tool safety: pass → unverified ▼ security
- TLS certificate: pass → unverified ▼ security
- Transport: pass → fail ▼ security
- Capabilities: pass → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- First check of Schema quality: unverified functional
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.
- 30 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.
- 27 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.
- 25 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.
- 23 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.
- 21 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Probed https://footprint.pharmatools.ai/mcp
TLS unreached
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of footprint.pharmatools.ai. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| ai. | present | 1814, 3799 | 8, 8 | Verified |
| pharmatools.ai. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication Inconclusive
We could not reach the endpoint well enough to judge its authorisation posture.
| Result | Inconclusive |
|---|
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://footprint.pharmatools.ai/mcp | Unreachable | ||
| http (plaintext) | http://footprint.pharmatools.ai/mcp | HTTPS enforced |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_water_use Put an amount of water in everyday context ~85
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.
| Name | Type | Req | Description |
|---|---|---|---|
| water_ml | number | yes | – |
No output schema declared.
No examples provided.
estimate_ai_footprint Estimate AI water & energy footprint ~207
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.
| Name | Type | Req | Description |
|---|---|---|---|
| boundary | string | – | – |
| days | integer | – | – |
| queries_per_day | number | yes | – |
| usage | string | – | – |
No output schema declared.
No examples provided.
estimate_session_footprint Estimate an AI session's footprint from token counts ~176
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.
| Name | Type | Req | Description |
|---|---|---|---|
| boundary | string | – | – |
| cached_input_tokens | integer | – | – |
| input_tokens | integer | yes | – |
| model_class | string | – | – |
| output_tokens | integer | yes | – |
No output schema declared.
No examples provided.
get_methodology AI water footprint methodology and sources ~71
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.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
How do I install the AI Footprint MCP server?
AI Footprint is a hosted endpoint at https://footprint.pharmatools.ai/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · footprint.pharmatools.ai
claude mcp add --transport http nickjlamb-ai-footprint 'https://footprint.pharmatools.ai/mcp'
What is the AI Footprint MCP server?
AI Footprint is an MCP server listed in the public MCP registry as io.github.nickjlamb/ai-footprint. Water and energy footprint of AI use: chat prompts, agent sessions, methodology and sources. This page covers its hosted endpoint (https://footprint.pharmatools.ai/mcp).
Is the AI Footprint MCP server safe to use?
AI Footprint scores 77 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the AI Footprint MCP server expose?
AI Footprint exposes 4 tools: estimate_ai_footprint, get_methodology, compare_water_use, estimate_session_footprint. Their descriptions and schemas cost roughly 539 tokens of context every time the server is loaded.
Does the AI Footprint MCP server require authentication?
Its publisher declares no required credentials for AI Footprint. We have not been able to confirm that against the live endpoint, and a server can require authorisation without declaring it here.
Is the AI Footprint MCP server still maintained?
AI Footprint is still listed in the MCP registry, though our most recent checks did not reach this channel. We last reached this channel on 3 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.