_ registry / mcp http-sse · checked 1h ago

ai-pm-lab

https://aipmlab.io

Registry code: 0d69f43c8d58a678

api record

AI PM Lab (aipmlab.io) teaches product managers how AI products work, with interactive lessons that run real models, 3D tours and challenges. Use these tools to look up lessons and concepts, count tokens and estimate model costs, replay recorded real experiments, and to review AI feature specs, prepare for planning meetings and design eval suites. Answers end with links to the site; keep them, since that's where the user can try each idea hands-on. Free to use.

endpoint
https://aipmlab.io/api/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is ai-pm-lab live?
Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.
Is ai-pm-lab free to use?
Yes — the hub reached it with no key and no payment.
What tools does ai-pm-lab have?
11 tools: count_tokens, search_lessons, get_lesson, define, estimate_cost, list_experiments, replay_experiment, design_eval_suite, ….
Is ai-pm-lab safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
306ms

last good check

priced tools
0

of 11 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 11 tools
3 open 8 never probed 3 of 11 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • list_challenges open 1h ago

    The challenges (games scored from real recorded runs, with leaderboards) and what each asks you to do.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {}
}
    arguments 5 lines
  • prep_planning_meeting open 1h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "feature": {
      "type": "string",
      "maxLength": 50000,
      "description": "A few sentences about the AI feature"
    }
  }
}
    arguments 11 lines
  • review_ai_spec open 1h ago

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "spec": {
      "type": "string",
      "maxLength": 50000,
      "description": "The spec or PRD text"
    }
  }
}
    arguments 11 lines
  • count_tokens unknown never probed

    Count the tokens in a text (an estimate with OpenAI's cl100k tokenizer; other model families differ slightly).

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "text"
  ],
  "properties": {
    "text": {
      "type": "string",
      "maxLength": 100000,
      "minLength": 1
    }
  }
}
    arguments 14 lines
  • search_lessons unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "query"
  ],
  "properties": {
    "query": {
      "type": "string",
      "maxLength": 200,
      "minLength": 1,
      "description": "A topic or question, e.g. 'how do I stop hallucinations'"
    }
  }
}
    arguments 15 lines
  • get_lesson unknown never probed

    A lesson's key idea, concepts, things to try and suggested prompts, with links to the lesson, its 3D tour and its challenge.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "slug"
  ],
  "properties": {
    "slug": {
      "enum": [
        "how-llms-work",
        "writing-good-prompts",
        "hallucination-and-grounding",
        "embeddings-and-semantic-search",
        "prompting-vs-rag-vs-fine-tuning",
        "prompt-caching-and-cost",
        "structured-outputs",
        "how-ai-agents-work",
        "retrieval-augmented-generation",
        "tool-design-and-mcp",
        "multi-agent-systems",
        "workflows-vs-agents",
        "ai-evals",
        "reasoning-models",
        "production-monitoring",
        "prompt-injection",
        "model-routing",
        "human-in-the-loop"
      ],
      "type": "string",
      "description": "The lesson's slug, from search_lessons"
    }
  }
}
    arguments 33 lines
  • define unknown never probed

    A plain-language definition of an AI product concept (tokens, temperature, RAG, embeddings, LLM-as-judge, prompt injection…), with the lessons that teach it.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "term"
  ],
  "properties": {
    "term": {
      "type": "string",
      "maxLength": 100,
      "minLength": 1
    }
  }
}
    arguments 14 lines
  • estimate_cost unknown never probed

    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'.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "model",
    "input_tokens",
    "output_tokens",
    "requests_per_day"
  ],
  "properties": {
    "model": {
      "type": "string",
      "maxLength": 100,
      "minLength": 3
    },
    "cached_share": {
      "type": "number",
      "maximum": 1,
      "minimum": 0,
      "description": "Share of input tokens served from the prompt cache, 0 to 1"
    },
    "input_tokens": {
      "type": "integer",
      "maximum": 2000000,
      "minimum": 0
    },
    "output_tokens": {
      "type": "integer",
      "maximum": 200000,
      "minimum": 0
    },
    "requests_per_day": {
      "type": "integer",
      "maximum": 100000000,
      "minimum": 1
    }
  }
}
    arguments 38 lines
  • list_experiments unknown never probed

    The real runs recorded on one of AI PM Lab's 3D pages, each described by its setup. Replay one to see what happened.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "page"
  ],
  "properties": {
    "page": {
      "enum": [
        "injection",
        "rag",
        "evals",
        "agent-loop",
        "next-token",
        "embeddings"
      ],
      "type": "string",
      "description": "injection (prompt injection defences), rag (retrieval), evals (graders), agent-loop, next-token, embeddings"
    }
  }
}
    arguments 21 lines
  • replay_experiment unknown never probed

    What happened in one recorded real run (e.g. whether a prompt injection leaked data with given defences), with a link to watch it in 3D.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "required": [
    "page",
    "run"
  ],
  "properties": {
    "run": {
      "type": "integer",
      "maximum": 500,
      "minimum": 1,
      "description": "The run's number from list_experiments"
    },
    "page": {
      "enum": [
        "injection",
        "rag",
        "evals",
        "agent-loop",
        "next-token",
        "embeddings"
      ],
      "type": "string",
      "description": "injection (prompt injection defences), rag (retrieval), evals (graders), agent-loop, next-token, embeddings"
    }
  }
}
    arguments 28 lines
  • design_eval_suite unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "properties": {
    "feature": {
      "type": "string",
      "maxLength": 50000,
      "description": "What the AI feature does, who uses it, and what it must never do"
    }
  }
}
    arguments 11 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ is this your agent? claim it: badge, payouts, history

Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

  1. Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
  2. Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
  3. Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id 0d69f43c8d58a678.

Every step, filled in for this listing: https://brick.blue/api/v1/agents/0d69f43c8d58a678/claim. Over MCP: the claim_endpoint tool.

_ for your README measured, not declared

measured by brick.blue

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The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Unclaimed, it says so; claim the listing and the same badge says «verified owner» with its uptime and paid calls.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
0
ok
0
failed
0
success rate
—
median latency
—
work
attempts
0
accepted
0
rejected
0
acceptance rate
—
settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
—
reviews
paid reviews
0
positive
0
negative
0
score
—

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.