bots.st/ai-pm-lab
AI PM Lab
AI product PM guidance and unit economics
2915 ms
answered our call
- State
- Answered
- Protocol
- MCP 2025-06-18
- Tools listed
- 18
- Answered
- 3 of the last 3 calls
How to reach it
- MCP, HTTP
https://aipmlab.io/api/mcp
What it can do
| Tool | What it says it does |
|---|---|
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… |
count_tokens | Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly). |
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. |
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 bars, and a JSON test set. Returns instructions to follow with the user's text. |
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 'anthropic/claude-sonnet-4.5'. |
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 away and questions to ask engineers, with links to the lesson, its 3D tour and its challenge. |
list_challenges | The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do. |
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 happened. |
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. 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… |
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 with the user's text. |
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 with margins, guardrails, and the assumptions to validate. Returns instructions to follow with the user's text. |
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 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… |
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): the setup, each step, the result and the model's reply, with a link to run it on the site. |
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, actions, and the new eval cases that would catch it next time. Returns instructions to follow with the user's text. |
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, evals, monitoring, fallbacks. Gaps link to lessons. Returns instructions to follow with the user's text. |
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 risk or missing, with blockers and a launch-day watch list. Returns instructions to follow with the user's 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. |
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 criteria, latency and cost budgets, and example test cases, ready to paste into a ticket. Returns instructions to follow with the user's text. |
In its maker's words
Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides.
- Website
- aipmlab.io/mcp
- Publisher
- aipmlab.io
- Listed from
- the official MCP Registry as
io.aipmlab/ai-pm-lab - Version
- 1.2.0, updated 12 Oct 2026
- Kinds of work
- AI models and agent tools, Writing and translation, Finance and markets
The domain it lives on
- aipmlab.io
- registered 6 Oct 2026 (1 month ago), at Name.com, Inc., its home page is a working site
From our own crawl of domain names: registry records and the home page as our crawler last saw it.
Every call we made
| When | What happened | Time |
|---|---|---|
| Answered | 2915 ms | |
| Answered | 1970 ms | |
| Answered | 1035 ms |
Next door
- WhiteMagic — governed local memory for agent continuityGoverned local agent memory reads and writes
- PolyrankTrader metrics, rankings, and strategy backtests
- DiffCIValidate DiffCI reports and plan affected tests
- FOGA nodeResolve agent identities and safety verdicts, simulate and r
- Mind Upload AI meta harnessRoute coding work through saved memories
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