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NotebookLM MCP Server

by dougrn

Notebook Describe

notebook_describe
Read-only

Fetch a notebook's AI-generated description by name or ID, returning the resolved notebook ID and summary. Optionally include metadata with details and sources.

Instructions

Fetch a notebook's AI-generated description. Accepts a notebook name or ID.

Returns the resolved notebook_id plus the AI description. Pass include_metadata=True to additionally fetch the notebook's metadata (details + source list) and surface it under a metadata key; the default output (include_metadata omitted) is unchanged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notebookYes
include_metadataNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds real value beyond that: it discloses the resolved return shape (``notebook_id`` plus ``description``), the conditional ``metadata`` key, and explicitly promises that the default output is unchanged when ``include_metadata`` is omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action in the first sentence, then the return contract and the optional flag. Mostly efficient, though the second sentence is slightly dense with formatting artifacts. No filler sentences that fail to earn their place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values need not be spelled out, yet the description still explains the resolved ID, description, and conditional metadata key, so nothing critical is missing. Minor gaps remain around error behavior when the notebook lacks a description or does not exist.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry the parameter burden, and it does: it clarifies that ``notebook`` accepts either a name or an ID (the schema only says 'string') and explains what ``include_metadata=True`` fetches and how it changes the response. It does not mention the boolean default of false directly, but that is covered by the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: fetch a notebook's AI-generated description from a name or ID. That distinguishes it from siblings like notebook_list, notebook_rename, and notebook_delete, though it never explicitly names an alternative. The purpose is clear without being tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by the purpose statement: you call it when you need a notebook's AI description. There is no stated when-not-to-use, no mention of prerequisites (e.g. notebook must exist or already have a generated description), and no routing to a sibling such as notebook_list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.