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8 clients · secrets stay placeholders

JSON (mcpServers)Claude CodeCodexOpenCodeCursorVS CodeGemini CLIGoose

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

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

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

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

{
  "mcpServers": {
    "ai-pm-lab": {
      "type": "http",
      "url": "https://aipmlab.io/api/mcp"
    }
  },
  "deeplink": "cursor://anysphere.cursor-deeplink/mcp/install?name=ai-pm-lab&config=eyJ0eXBlIjoiaHR0cCIsInVybCI6Imh0dHBzOi8vYWlwbWxhYi5pby9hcGkvbWNwIn0%3D"
}

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

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

extensions:
  "ai-pm-lab":
    enabled: true
    name: "ai-pm-lab"
    type: streamable_http
    uri: "https://aipmlab.io/api/mcp"
    timeout: 300

Quality 79 / 100good

protocol88 · weight 25

pass
protocol.modernsupports 2026-07-28 (server/discover)
pass
protocol.statelessanswers without a session
pass
protocol.transportstreamable HTTP
warn
protocol.list_ttltools/list declares ttlMs 0, so clients re-fetch it on every use

authorizationn/a · weight 15

n/a
auth.prm, auth.as_metadata, auth.cimdno remote uses OAuth
n/a
auth.secret_in_urlno templated URL

tool hygiene100 · weight 30

pass
tools.descriptionsevery tool has a description
pass
tools.description_lengthdescriptions are concise
pass
tools.schemasevery tool has an object input schema
pass
tools.annotations18 of 18 tools declare readOnlyHint or destructiveHint
pass
tools.directory_hintsevery tool declares readOnlyHint, destructiveHint, idempotentHint and openWorldHint
pass
tools.token_costabout 3,332 tokens to load every tool
pass
tools.api_dumptools are not a one-to-one API dump

stability25 · weight 15

warn
stability.changes32 tool changes in 30 days
fail
stability.rug_pull2 tools kept their name but changed most of their description in 30 days; review before trusting them

dependenciesn/a · weight 15

n/a
deps.known_vulns, deps.mcp_sdk_version, deps.resolvableno npm or PyPI package

18 tools, about 3,332 tokens to load them all · tool set 098bd1d6b49c · tools last changed 2026-10-17 · checked 2026-10-19 14:10 UTC

Tool changes

2026-10-17get_lessonchangeddescriptionmeaning changed
before
A lesson's key idea, concepts, things to try and suggested prompts, with links to the lesson, its 3D tour and its challenge.
after · 43% of words in common
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.
2026-10-12launch_readiness_reviewremoved
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.
2026-10-12defineremoved
A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.
2026-10-12ai_incident_reviewremoved
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.
2026-10-12review_ai_incidentadded
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.
2026-10-12review_launch_readinessadded
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.
2026-10-12plan_ai_launchadded
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.
2026-10-12price_ai_featurechangedoutput schema
2026-10-12write_acceptance_criteriachangedoutput schema
2026-10-12design_eval_suitechangedoutput schema

Showing the latest 10. Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.

Trust 83 / 100

no-repository

provenance55

liveness100

freshness100

hygiene80

+dns-namespace +website +reachable +uptime-100% +updated-7d-ago -no-repository

Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. How it is computed.

Endpoints

remote	auth	reachable	uptime 30d	p50	protocol	last ok
`https://aipmlab.io/api/mcp` streamable-http	none	yes	100%	441 ms	2026-07-28	2026-10-19

Tools (18)

`compare_models`read-only	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.
`count_tokens`read-only	Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).
`define_term`read-only	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`read-only	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`read-only	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`read-only	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`read-only	The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.
`list_experiments`read-only	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`read-only	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.
`prep_planning_meeting`read-only	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`read-only	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`read-only	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.
`replay_experiment`read-only	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`read-only	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`read-only	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`read-only	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`read-only	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`read-only	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.

Schemas: list_tools.

Similar MCP servers

Official server.json

{
  "name": "io.aipmlab/ai-pm-lab",
  "icons": [
    {
      "src": "https://aipmlab.io/icon/512",
      "sizes": [
        "512x512"
      ],
      "mimeType": "image/png"
    }
  ],
  "title": "AI PM Lab",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "remotes": [
    {
      "url": "https://aipmlab.io/api/mcp",
      "type": "streamable-http"
    }
  ],
  "version": "1.2.0",
  "websiteUrl": "https://aipmlab.io/mcp",
  "description": "Learn how AI products work: lessons, model cost comparisons, real experiments, PM guides."
}