Node.js/TypeScript MCP server for Atlassian Confluence. Provides tools enabling AI systems (LLMs) to list/get spaces & pages (content
Nothing to run from here
AI PM Lab is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
18
tools declared
—
trust score
remote
at its publisher
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calls last 7d
What it does
Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http ai-pm-lab-mcp https://aipmlab.io/api/mcp
# 2 — ask your agent for something these tools do
# search_lessons, get_lesson, list_challenges and 15 more
AI PM Lab costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
AIAll 1 server
aipmlab1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. Nothing here runs on your machine and nothing runs on ours: this server answers at its publisher’s own address, and what you install is the connection straight to it.
Claude Codedeclared by this listing
This is a public server: it answers at its publisher’s own address and this marketplace is not in the path. Claude Code registers it in one command; --scope user makes it available in every project. The transport below is http, as the publisher's own manifest declares it.
claude mcp add --scope user --transport http ai-pm-lab-mcp https://aipmlab.io/api/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it answers at its publisher’s own address: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry pointing straight at the publisher’s own address — this marketplace is not in the path, and nothing from this listing executes on your machine.
What you approve
18 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| query | string | yes | A topic or question, e.g. 'how do I stop hallucinations' |
A lesson's key idea, concepts, things to try and suggested prompts, with links to the lesson, its 3D tour and its challenge.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| slug | string | yes | The lesson's slug, from search_lessons |
The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.
No parameter schema on file.
A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| term | string | yes | The AI term to define, e.g. 'RAG', 'temperature' or 'prompt caching' |
Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| text | string | yes | The text to count, such as a system prompt or a typical user message |
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'.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| model | string | yes | A model id like 'openai/gpt-4.1-mini' or 'anthropic/claude-sonnet-4' |
| input_tokens | integer | yes | Input tokens per request: system prompt, retrieved documents, history and the user's message |
| 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 |
| cached_share | number | no | Share of input tokens served from the prompt cache, 0 to 1 |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| models | array | yes | Model ids like 'openai/gpt-4.1-mini', or provider names like 'anthropic' for that provider's newest models |
| input_tokens | integer | yes | Input tokens per request: system prompt, retrieved documents, history and the user's message |
| 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 |
| cached_share | number | no | Share of input tokens served from the prompt cache, 0 to 1 |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| 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) |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| 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 |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| game | string | no | call-it, spot-the-failure or which-wins; any if left out |
| round | string | no | The id of the round the user is answering, from the last quiz_me result |
| answer | string | no | The user's choice for that round: its letter |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| spec | string | no | The spec or PRD text |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| feature | string | no | A few sentences about the AI feature |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| feature | string | no | What the AI feature does, who uses it, and what it must never do |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| feature | string | no | The AI feature or user story, who uses it, and what it must never do |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| feature | string | no | What the feature does, the model it uses, expected usage, and how the product is priced today |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| feature | string | no | What the AI feature does, who uses it, what it must never do, and where the work stands today |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| plan | string | no | The feature, what was tested, and the launch plan |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| incident | string | no | What the feature does, what went wrong, and what's known so far |
Free
$0 forever
- Every tool — any call limit is its publisher’s, not ours
- No card, and mcprush charges nothing for it
- Runs at its publisher’s own address — this marketplace is not in the path
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install AI PM Lab or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
—Grade not published
No rule-by-rule report on file for this release
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
This listing is an address its publisher runs, and you connect to it directly — this marketplace is not in the path. The scanner reads packages and public source, and an address is neither. So there is no grade — not a withheld one, an unmeasured one.
Release history
This is an address its publisher runs: which release it serves is theirs to choose, so there is nothing here to pin. The history below is for reading; the current release on file is 1.2.0.
1.2.0
undated
latestNo release note was published with this version.
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no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish AI PM Lab can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.