AI PM Lab
REMOTE · AIPMLAB.IO
Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides.
Available components
Last scanned (23 hours ago)
Scores higher than 89% of remote servers
Reachable. Last reached 4 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 Security80
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one. See how to fix → View diagnostics → Partial
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability83
- 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).Pass
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 2683 tokens (~74/item across 36 items; 18 tools + 18 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management77
- Stability observed for 23 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "design_eval_suite" implies "eval" and declares readOnlyHint instead, contradicting what its own name says it does. See how to fix → Fail
- An AI judge read all 20 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
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.
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 70 to 73. That category is still filling its 30-day observation window: 21 days of observed history at the previous scan, 22 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 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 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 57 to 60. That category is still filling its 30-day observation window: 17 days of observed history at the previous scan, 18 at this one. The score rises as the window fills, whether or not the server changes.
- 28 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 50 to 53. That category is still filling its 30-day observation window: 15 days of observed history at the previous scan, 16 at this one. The score rises as the window fills, whether or not the server changes.
- 26 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.
- 23 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 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 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.
- 19 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 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 4 Nov 2026 · Probed https://aipmlab.io/api/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=*.aipmlab.io | CN=YR1,O=Let's Encrypt,C=US | 7 Oct 2026 | 5 Jan 2027 | RSA 2048 | SHA256-RSA | 577f76e8135f1ed33bf9b9e6ea1b4a36b8c |
| SANs: *.aipmlab.io, aipmlab.io | ||||||
| CN=YR1,O=Let's Encrypt,C=US (CA) | CN=Root YR,O=ISRG,C=US | 3 Sept 2025 | 2 Sept 2028 | RSA 2048 | SHA256-RSA | a20253f15f2691c05dc1ce13b9bcca4e |
| CN=Root YR,O=ISRG,C=US (CA) | CN=ISRG Root X1,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | RSA 4096 | SHA256-RSA | f24b6d17f9d9ad7cb1c9fea78782699f |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of aipmlab.io. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| io. | present | 38690 | 8 | Verified |
| aipmlab.io. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
| Header | Value |
|---|---|
| strict-transport-security | max-age=63072000 |
| content-security-policy | default-src 'self'; script-src 'self' 'unsafe-inline'; style-src 'self' 'unsafe-inline'; img-src 'self' blob: data:; font-src 'self'; connect-src 'self'; media-src 'self'; object-src 'none'; base-uri 'self'; form-action 'self'; frame-ancestors 'none'; upgrade-insecure-requests |
| x-content-type-options | nosniff |
| x-frame-options | DENY |
| referrer-policy | strict-origin-when-cross-origin |
| permissions-policy | camera=(), microphone=(), geolocation=(), payment=(), usb=() |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://aipmlab.io/api/mcp | Verified | 200 | |
| http (plaintext) | http://aipmlab.io/api/mcp | HTTPS enforced | 308 | https://aipmlab.io/api/mcp |
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_models Compare models ~211
Compare several models side by side at a given volume: cost per request and per month, prices, context window, output limit, release date and knowledge cutoff, from AI Gateway's live catalog. Pass ids like 'openai/gpt-4.1-mini', or a provider name ('anthropic', 'openai', 'google') for its newest models, so you don't need to know which models are current.
| Name | Type | Req | Description |
|---|---|---|---|
| cached_share | number | – | Share of input tokens served from the prompt cache, 0 to 1 |
| input_tokens | integer | yes | Input tokens per request: system prompt, retrieved documents, history and the user's message |
| models | array | yes | Model ids like 'openai/gpt-4.1-mini', or provider names like 'anthropic' for that provider's newest models |
| output_tokens | integer | yes | Output tokens per request: the length of a typical reply |
| requests_per_day | integer | yes | Expected requests per day, for the monthly cost |
| Name | Type | Req | Description |
|---|---|---|---|
| live_catalog | boolean | yes | – |
| models | array | yes | – |
| requests_per_day | number | yes | – |
| unknown | array | yes | – |
No examples provided.
count_tokens Count tokens ~54
Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | The text to count, such as a system prompt or a typical user message |
| Name | Type | Req | Description |
|---|---|---|---|
| characters | number | yes | – |
| tokenizer | string | yes | – |
| tokens | number | yes | – |
No examples provided.
define_term Define an AI concept ~70
A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.
| Name | Type | Req | Description |
|---|---|---|---|
| term | string | yes | The AI term to define, e.g. 'RAG', 'temperature' or 'prompt caching' |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
design_eval_suite Design an eval suite ~86
Build an evaluation suite for a planned AI feature with AI PM Lab's method: test cases (typical, edge, out of scope, attacks), graders with a judge rubric, pass bars, and a JSON test set. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| feature | string | – | What the AI feature does, who uses it, and what it must never do |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
estimate_cost Estimate model cost ~179
What a model call costs per request, per day and per month at a given volume, from AI Gateway's live prices. Model ids look like 'openai/gpt-4.1-mini' or 'anthropic/claude-sonnet-4.5'.
| Name | Type | Req | Description |
|---|---|---|---|
| cached_share | number | – | Share of input tokens served from the prompt cache, 0 to 1 |
| input_tokens | integer | yes | Input tokens per request: system prompt, retrieved documents, history and the user's message |
| model | string | yes | A model id like 'openai/gpt-4.1-mini' or 'anthropic/claude-sonnet-4' |
| output_tokens | integer | yes | Output tokens per request: the length of a typical reply |
| requests_per_day | integer | yes | Expected requests per day, for the monthly cost |
| Name | Type | Req | Description |
|---|---|---|---|
| cache_saving_per_month | number | yes | – |
| input_share | number | yes | Share of the cost that's input tokens, 0 to 1 |
| model | string | yes | – |
| per_day | number | yes | – |
| per_month | number | yes | – |
| per_request | number | yes | – |
| price_source | string | yes | – |
| prices_per_million | object | yes | Dollars per million tokens |
No examples provided.
get_lesson Get an AI PM Lab lesson ~82
A lesson in full: the product decision it informs, its key idea and concepts, things to try with what to notice in each (from recorded runs), what a PM takes away and questions to ask engineers, with links to the lesson, its 3D tour and its challenge.
| Name | Type | Req | Description |
|---|---|---|---|
| slug | string | yes | The lesson's slug, from search_lessons |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
list_challenges List AI PM Lab challenges ~31
The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
list_experiments List recorded experiments ~107
The real runs recorded on one of AI PM Lab's 3D pages or lessons, each described by its setup (e.g. which defences or settings were on). Replay one to see what happened.
| Name | Type | Req | Description |
|---|---|---|---|
| page | string | yes | A 3D page (injection, rag, evals, agent-loop, next-token, embeddings) or a lesson's slug (e.g. prompt-injection, model-routing, hallucination-and-grounding, human-in-the-loop) |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
plan_ai_launch Plan an AI feature's road to launch ~120
A phased roadmap from where an AI feature is today to launch and its first month live: frame, prototype and model, evals, hardening, pilot, launch, monitoring. Each phase has exit criteria and the tests and checks for this feature, backed by findings from AI PM Lab's recorded real runs (injection, grounding, evals, agent cost). Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| feature | string | – | What the AI feature does, who uses it, what it must never do, and where the work stands today |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
prep_planning_meeting Prepare for an AI planning meeting ~59
The questions a PM should ask engineers about a planned AI feature, with why each matters and what a good answer sounds like. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| feature | string | – | A few sentences about the AI feature |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
price_ai_feature Price an AI feature ~90
Work out an AI feature's unit economics and pricing: cost per request and per user (light, typical, heavy), levers like caching and routing, pricing options with margins, guardrails, and the assumptions to validate. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| feature | string | – | What the feature does, the model it uses, expected usage, and how the product is priced today |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
quiz_me Quiz me on real AI results ~153
A round from AI PM Lab's challenges, built from real recorded runs: Call It (predict what a model did), Spot the Failure (ship or flag a real output) or Which Wins? (pick the better of two setups). Returns the question without its answer; call again with the round id and the user's choice to mark it, see how players on the site called it, and get the next round.
| Name | Type | Req | Description |
|---|---|---|---|
| answer | string | – | The user's choice for that round: its letter |
| game | string | – | call-it, spot-the-failure or which-wins; any if left out |
| round | string | – | The id of the round the user is answering, from the last quiz_me result |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
replay_experiment Replay a recorded experiment ~138
What happened in one recorded real run (e.g. whether a prompt injection leaked data with given defences, or how many answers were made up without a facts sheet): the setup, each step, the result and the model's reply, with a link to run it on the site.
| Name | Type | Req | Description |
|---|---|---|---|
| page | string | yes | A 3D page (injection, rag, evals, agent-loop, next-token, embeddings) or a lesson's slug (e.g. prompt-injection, model-routing, hallucination-and-grounding, human-in-the-loop) |
| run | integer | yes | The run's number from list_experiments |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
review_ai_incident Write up an AI incident ~92
A blameless post-incident review for an AI feature: where it failed (input, retrieval, prompt, model, tools, output checks), why evals and monitoring missed it, actions, and the new eval cases that would catch it next time. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| incident | string | – | What the feature does, what went wrong, and what's known so far |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
review_ai_spec Review an AI feature spec ~80
Check an AI feature spec or PRD against what AI PM Lab teaches: approach, model and cost, prompt, grounding, output contract, tools, security, human oversight, evals, monitoring, fallbacks. Gaps link to lessons. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| spec | string | – | The spec or PRD text |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
review_launch_readiness Review an AI feature's launch readiness ~87
A go/no-go review of an AI feature's launch plan: evals, grounding, attacks, approvals, cost, latency, monitoring, rollout and support, each marked ready, at risk or missing, with blockers and a launch-day watch list. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| plan | string | – | The feature, what was tested, and the launch plan |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
search_lessons Search AI PM Lab lessons ~67
Find AI PM Lab lessons on a topic (e.g. RAG, evals, agents, prompt injection, cost). Returns the best matches with their key idea and link.
| Name | Type | Req | Description |
|---|---|---|---|
| query | string | yes | A topic or question, e.g. 'how do I stop hallucinations' |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
write_acceptance_criteria Write acceptance criteria for an AI feature ~95
Acceptance criteria for an AI feature or user story that can actually be tested: Given/When/Then with measurable pass bars on a test set, must-pass safety criteria, latency and cost budgets, and example test cases, ready to paste into a ticket. Returns instructions to follow with the user's text.
| Name | Type | Req | Description |
|---|---|---|---|
| feature | string | – | The AI feature or user story, who uses it, and what it must never do |
| Name | Type | Req | Description |
|---|---|---|---|
| links | array | yes | The AI PM Lab pages the answer links to, where each idea can be tried hands-on |
| text | string | yes | The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text |
No examples provided.
How do I install the AI PM Lab MCP server?
AI PM Lab is a hosted endpoint at https://aipmlab.io/api/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 · aipmlab.io
claude mcp add --transport http io-aipmlab-ai-pm-lab 'https://aipmlab.io/api/mcp'
What is the AI PM Lab MCP server?
AI PM Lab is an MCP server listed in the public MCP registry as io.aipmlab/ai-pm-lab. Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides. This page covers its hosted endpoint (https://aipmlab.io/api/mcp).
Is the AI PM Lab MCP server safe to use?
AI PM Lab scores 84 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 PM Lab MCP server expose?
AI PM Lab exposes 18 tools: search_lessons, get_lesson, list_challenges, define_term, count_tokens, and 13 more. Their descriptions and schemas cost roughly 1,801 tokens of context every time the server is loaded.
Does the AI PM Lab MCP server require authentication?
No. We connected to AI PM Lab without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the AI PM Lab MCP server still maintained?
AI PM Lab is still listed as active in the MCP registry. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.