io.aipmlab/ai-pm-lab

up checked 2026-11-11T10:39:17.024Z

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Publisher-provided title

AI PM Lab

Publisher-provided description

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

Summary by lookupd

Provides lessons, AI model cost comparisons and token counting, plus eval-suite and launch-readiness reviews for AI product work.

Install

Claude Code, remote (streamable-http)

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

Codex, remote (streamable-http)

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

Cursor, remote (streamable-http) → .cursor/mcp.json

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

Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.

Tools (18 tools)

Publisher-provided text

• search_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.
• get_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 aw
• list_challenges The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.
• define_term A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.
• count_tokens Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).
• estimate_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 'anthr
• 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,
• list_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
• replay_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)
• quiz_me 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 W
• review_ai_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,
• prep_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 wit
• design_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
• write_acceptance_criteria 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 crite
• price_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 wit
• plan_ai_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.
• review_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 ri
• review_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,

Details

• Version: 1.2.0
• Transports: streamable-http
• Uptime (7 days): 100%
• Median latency: 429 ms
• First indexed: 2026-11-01T09:02:49.586Z
• Last seen in registry: 2026-11-01T09:02:49.614Z
• Website: https://aipmlab.io/mcp

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