MCP server

AI PM Lab Official

io.aipmlab/ai-pm-lab

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website

At a glance

Type

MCP server

Publisher

aipmlab.io (verified by the MCP Registry)

Status

Working at last check

Tools

18

Token cost

About 3,586 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

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

Gemini CLI

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

Cursor

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

VS Code

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

Antigravity

agy mcp add ai-pm-lab https://aipmlab.io/api/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ai-pm-lab","server_url":"https://aipmlab.io/api/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ai-pm-lab","server_url":"https://aipmlab.io/api/mcp"}

Tools

• compare_models ~396 tokens
  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 mod
• count_tokens ~120 tokens
  Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).
• define_term ~132 tokens
  A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.
• design_eval_suite ~148 tokens
  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.
• estimate_cost ~320 tokens
  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'.
• get_lesson ~262 tokens
  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.
• list_challenges ~72 tokens
  The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.
• list_experiments ~298 tokens
  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.
• plan_ai_launch ~196 tokens
  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, g
• prep_planning_meeting ~124 tokens
  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.
• price_ai_feature ~164 tokens
  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.
• quiz_me ~254 tokens
  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
• replay_experiment ~354 tokens
  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.
• review_ai_incident ~159 tokens
  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.
• review_ai_spec ~141 tokens
  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.
• review_launch_readiness ~153 tokens
  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.
• search_lessons ~125 tokens
  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.
• write_acceptance_criteria ~168 tokens
  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.

Browse

• Claude tools
• Searching your documents (RAG)
• Calling other AI models

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