INDIVIDUAL MCP TOOL

count_tokens

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

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 count_tokens and invoke it with your own arguments.

{}

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.

CONNECT WITH APPROVAL

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.

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.
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.
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.