Skip to main content
Glama
juanQNav

mcp-notebooklm

by juanQNav
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_notebooksA

List all available NotebookLM notebooks with their IDs, titles, and source counts.

find_notebookA
Find NotebookLM notebooks by title (partial match, case-insensitive).

Args:
    title: The title or partial title to search for.
ask_notebookA
Ask a question to a specific NotebookLM notebook and get an AI answer
based on its sources.

Args:
    notebook_id: The ID of the notebook to query
    (use list_notebooks to find IDs).
    question: The question to ask the notebook.
generate_quizA
Generate a quiz (JSON) from a NotebookLM notebook's sources.

Generates questions in batches to bypass NotebookLM's ~20 question
limit. Supports multiple_choice and true_false question types.

Args:
    notebook_id: The notebook to generate questions from.
    num_questions: Total number of questions to generate.
    topic: Specific topic or "all sources" for everything.
    difficulty: easy, medium, hard, or mixed.
    output_path: Optional file path to save the JSON quiz.
    cumulative: If true and output_path exists, merge with existing.
    language: Language for questions (default: "es").

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a distinct, non-overlapping purpose: listing notebooks, finding by title, asking questions, and generating quizzes. No ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (list_notebooks, find_notebook, ask_notebook, generate_quiz), making predictions easy.

Tool Count5/5

With 4 tools, the server is well-scoped for interacting with NotebookLM notebooks—covering essential operations without excess.

Completeness4/5

Covers listing, searching, asking, and quiz generation. Minor gaps like notebook creation/deletion or source retrieval, but these are outside the server's apparent focus.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources