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create_notebook

Start a new project by creating a blank notebook in NotebookLM. Obtain its URL and ID for subsequent actions like adding sources or asking questions.

Instructions

Create a brand-new empty notebook directly in NotebookLM (no pre-existing URL required, unlike add_notebook which only registers an already-created notebook into the library).

Returns { notebook_url, notebook_id, name_applied, actual_name, message }.

  • notebook_url / notebook_id: always the FINAL UUID-based URL (the tool waits past the /notebook/creating/c transitional URL).

  • name_applied (boolean): whether the name parameter actually took effect. false means the notebook is still "Untitled notebook" — rename via UI if needed.

  • actual_name (string): the title observed on the notebook after creation.

Typical workflow:

  1. create_notebook({ name?: "my-research" }){ notebook_url, notebook_id, name_applied, actual_name }

  2. add_source({ notebook_url, source_type: "url", source: "https://..." })

  3. ask_question({ notebook_url, question: "..." })

Note: Requires authentication. Run setup_auth first if not authenticated.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional initial title for the notebook. NotebookLM will auto-name it if omitted (usually "Untitled notebook"). The title can be edited later via the UI.
show_browserNoShow browser window during creation. Default: false (headless).
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the return structure, the transitional URL handling, and the behavior of the 'name' parameter (boolean indication). Does not cover error cases or rate limits, but for a create tool, this level is good.

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?

The description is well-structured with bullet points and a workflow example. It is concise but includes some repetition (return object explained twice). Still, it is easy to parse and front-loaded.

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

Completeness5/5

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

Given the tool's simplicity (2 optional params, no output schema), the description thoroughly covers purpose, usage, return format, and workflow. It addresses potential confusion (name_applied) and differentiates from siblings, making it complete for an AI agent.

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 100%, so baseline is 3. The description adds value by explaining the return object and the detailed behavior of the 'name' parameter (name_applied boolean), going beyond the schema's basic description.

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

Purpose5/5

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

The description clearly states 'Create a brand-new empty notebook directly in NotebookLM' and explicitly contrasts with 'add_notebook' which only registers an existing notebook. The verb and resource are specific and unmistakable.

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

Usage Guidelines4/5

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

Provides clear differentiation from sibling 'add_notebook' and outlines a typical workflow sequence. Also notes authentication requirement. However, lacks explicit when-not-to-use scenarios beyond the sibling comparison.

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

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