# AI PM Lab (remote · aipmlab.io)

Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides.

- Trust score: 85/100 (high trust)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- remote · `aipmlab.io`: 85/100 (this document), [markdown](https://verifymcp.io/servers/io-aipmlab-ai-pm-lab/api-mcp.md), [page](https://verifymcp.io/servers/io-aipmlab-ai-pm-lab/api-mcp)

## Channel facts

- Endpoint: `https://aipmlab.io/api/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.2.0`

## Trust breakdown

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. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-05.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 83/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 2683 tokens (~74/item across 36 items; 18 tools + 18 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 80/100
  - Stability observed for 24 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 75/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - 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.
  - An AI judge read all 20 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### 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.

### Claude

```bash
claude mcp add --transport http io-aipmlab-ai-pm-lab 'https://aipmlab.io/api/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "io-aipmlab-ai-pm-lab": {
      "url": "https://aipmlab.io/api/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "io-aipmlab-ai-pm-lab": {
      "type": "http",
      "url": "https://aipmlab.io/api/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.io-aipmlab-ai-pm-lab]
url = "https://aipmlab.io/api/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "io-aipmlab-ai-pm-lab": {
      "type": "remote",
      "url": "https://aipmlab.io/api/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add io-aipmlab-ai-pm-lab --url 'https://aipmlab.io/api/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  io-aipmlab-ai-pm-lab:
    url: "https://aipmlab.io/api/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "io-aipmlab-ai-pm-lab": {
      "Transport": "http",
      "Url": "https://aipmlab.io/api/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add io-aipmlab-ai-pm-lab -t streamable-http -u 'https://aipmlab.io/api/mcp'
```

### Other

```json
{
  "mcpServers": {
    "io-aipmlab-ai-pm-lab": {
      "type": "http",
      "url": "https://aipmlab.io/api/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-05 (score 85, +1)

No change was recorded against any check on this day. Stability & Change Management went from 77 to 80. That category is still filling its 30-day observation window: 23 days of observed history at the previous scan, 24 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-03 (score 84, +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.

### 2026-11-01 (score 83, +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.

### 2026-10-30 (score 82, +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.

### 2026-10-28 (score 81, +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.

### 2026-10-26 (score 80, +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.

### 2026-10-23 (score 79, +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.

### 2026-10-21 (score 78, +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.

## MCP tools (18)

### `search_lessons` (~67 tokens)

Search AI PM Lab lessons

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.

Input parameters:

- `query` (string, required): A topic or question, e.g. 'how do I stop hallucinations'

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `get_lesson` (~82 tokens)

Get an AI PM Lab lesson

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.

Input parameters:

- `slug` (string, required): The lesson's slug, from search_lessons

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `list_challenges` (~31 tokens)

List AI PM Lab challenges

The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `define_term` (~70 tokens)

Define an AI concept

A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.

Input parameters:

- `term` (string, required): The AI term to define, e.g. 'RAG', 'temperature' or 'prompt caching'

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `count_tokens` (~54 tokens)

Count tokens

Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).

Input parameters:

- `text` (string, required): The text to count, such as a system prompt or a typical user message

Output parameters:

- `characters` (number)
- `tokenizer` (string)
- `tokens` (number)

### `estimate_cost` (~179 tokens)

Estimate model cost

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'.

Input parameters:

- `cached_share` (number): Share of input tokens served from the prompt cache, 0 to 1
- `input_tokens` (integer, required): Input tokens per request: system prompt, retrieved documents, history and the user's message
- `model` (string, required): A model id like 'openai/gpt-4.1-mini' or 'anthropic/claude-sonnet-4'
- `output_tokens` (integer, required): Output tokens per request: the length of a typical reply
- `requests_per_day` (integer, required): Expected requests per day, for the monthly cost

Output parameters:

- `cache_saving_per_month` (number)
- `input_share` (number): Share of the cost that's input tokens, 0 to 1
- `model` (string)
- `per_day` (number)
- `per_month` (number)
- `per_request` (number)
- `price_source` (string)
- `prices_per_million` (object): Dollars per million tokens

### `compare_models` (~211 tokens)

Compare models

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.

Input parameters:

- `cached_share` (number): Share of input tokens served from the prompt cache, 0 to 1
- `input_tokens` (integer, required): Input tokens per request: system prompt, retrieved documents, history and the user's message
- `models` (array, required): Model ids like 'openai/gpt-4.1-mini', or provider names like 'anthropic' for that provider's newest models
- `output_tokens` (integer, required): Output tokens per request: the length of a typical reply
- `requests_per_day` (integer, required): Expected requests per day, for the monthly cost

Output parameters:

- `live_catalog` (boolean)
- `models` (array)
- `requests_per_day` (number)
- `unknown` (array)

### `list_experiments` (~107 tokens)

List recorded experiments

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.

Input parameters:

- `page` (string, required): 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)

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `replay_experiment` (~138 tokens)

Replay a recorded experiment

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.

Input parameters:

- `page` (string, required): 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, required): The run's number from list_experiments

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `quiz_me` (~153 tokens)

Quiz me on real AI results

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.

Input parameters:

- `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

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `review_ai_spec` (~80 tokens)

Review an AI feature spec

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.

Input parameters:

- `spec` (string): The spec or PRD text

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `prep_planning_meeting` (~59 tokens)

Prepare for an AI planning meeting

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.

Input parameters:

- `feature` (string): A few sentences about the AI feature

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `design_eval_suite` (~86 tokens)

Design an eval suite

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.

Input parameters:

- `feature` (string): What the AI feature does, who uses it, and what it must never do

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `write_acceptance_criteria` (~95 tokens)

Write acceptance criteria for an AI feature

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.

Input parameters:

- `feature` (string): The AI feature or user story, who uses it, and what it must never do

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `price_ai_feature` (~90 tokens)

Price an AI feature

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.

Input parameters:

- `feature` (string): What the feature does, the model it uses, expected usage, and how the product is priced today

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `plan_ai_launch` (~120 tokens)

Plan an AI feature's road to launch

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.

Input parameters:

- `feature` (string): What the AI feature does, who uses it, what it must never do, and where the work stands today

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `review_launch_readiness` (~87 tokens)

Review an AI feature's launch readiness

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.

Input parameters:

- `plan` (string): The feature, what was tested, and the launch plan

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

### `review_ai_incident` (~92 tokens)

Write up an AI incident

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.

Input parameters:

- `incident` (string): What the feature does, what went wrong, and what's known so far

Output parameters:

- `links` (array): The AI PM Lab pages the answer links to, where each idea can be tried hands-on
- `text` (string): The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/io-aipmlab-ai-pm-lab/api-mcp#diagnostics

## Score history

- 2026-11-05: 85
- 2026-11-04: 84
- 2026-11-03: 84
- 2026-11-02: 83
- 2026-11-01: 83
- 2026-10-31: 82
- 2026-10-30: 82
- 2026-10-29: 81
- 2026-10-28: 81
- 2026-10-27: 80
- 2026-10-26: 80
- 2026-10-25: 79
- 2026-10-24: 79
- 2026-10-23: 79
- 2026-10-22: 78
- 2026-10-21: 78
- 2026-10-20: 77
- 2026-10-19: 77
- 2026-10-18: 76
- 2026-10-17: 76
- 2026-10-16: 75
- 2026-10-15: 75
- 2026-10-14: 74
- 2026-10-13: 74
- 2026-10-12: 73

## Common questions

### 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 85 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 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://aipmlab.io/api/mcp
- Website: https://aipmlab.io/mcp
- Changelog RSS feed: https://verifymcp.io/servers/io-aipmlab-ai-pm-lab/api-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/io-aipmlab-ai-pm-lab/api-mcp.json
- HTML version of this page: https://verifymcp.io/servers/io-aipmlab-ai-pm-lab/api-mcp