INDIVIDUAL MCP TOOL

define

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

aipmlab.ionone authenticationAvailability not checked

LIVE ENDPOINT

https://aipmlab.io/api/mcp

No auth detected

Connect to this endpoint to inspect the live schema for define and invoke it with your own arguments.

Indexed input schema

{}

Risk classification

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

Parent server

aipmlab.io

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

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

Claude Code

.mcp.json

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

Claude Desktop

Settings → Connectors → Add custom connector

Name: ai-pm-lab
Remote MCP URL: https://aipmlab.io/api/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

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

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

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

Generic MCP

Client-specific MCP configuration

{
  "name": "ai-pm-lab",
  "transport": "streamable-http",
  "url": "https://aipmlab.io/api/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

Related tools

search_lessons — Find AI PM Lab lessons on a topic (e.
get_lesson — A lesson's key idea, concepts, things to try and suggested prompts, 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.
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.
list_experiments — The real runs recorded on one of AI PM Lab's 3D pages, each described by its setup.
replay_experiment — What happened in one recorded real run (e.
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.