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note.to_source

Convert a note into a source document for NotebookLM, enabling its content to be used in RAG queries and research. Simply provide the note title.

Instructions

Convert a note to a source document in NotebookLM.

This feature allows you to convert an existing note into a source, making the note content available for RAG queries and research.

The method:

  1. Finds the note by title in the Studio panel

  2. Attempts to use NotebookLM's native "Convert to source" feature if available

  3. Falls back to extracting note content and creating a text source if not

Use this when you want your note content to be included in NotebookLM's knowledge base for answering questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
note_titleYesTitle of the note to convert (required)
notebook_urlNoNotebook URL. If not provided, uses the active notebook.
session_idNoSession ID to reuse an existing session

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successYesWhether the tool call succeeded.
dataNoThe tool payload on success. The exact shape depends on the tool.
errorNoHuman-readable error message, present only when success is false.
Behavior4/5

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

The description goes beyond annotations by detailing the conversion method steps, including the fallback mechanism to extract content and create a text source. This adds meaningful behavioral context beyond what readOnlyHint=false and openWorldHint=true already indicate.

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

Conciseness5/5

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

The description is concise and front-loaded with the main purpose. Every sentence adds value, with numbered steps for method clarity. There is no extraneous text, and the structure efficiently conveys the essential information.

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?

Given the presence of an output schema, return values need not be explained. The description covers the conversion process and fallback but could mention prerequisites (e.g., note existence) and side effects on the original note. Still largely complete.

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

Parameters3/5

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

The input schema has 100% description coverage, providing clear explanations for all three parameters. The tool description does not add any extra semantic meaning beyond verifying that note_title is used to find the note. Baseline score of 3 is appropriate.

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 explicitly states 'Convert a note to a source document in NotebookLM,' providing a clear verb and resource. It differentiates from sibling tools like note.create or source.add by detailing the conversion process and using NoteBookLM's native feature.

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?

The last sentence advises 'Use this when you want your note content to be included in NotebookLM's knowledge base for answering questions,' offering clear context. While it does not explicitly exclude alternatives or mention when not to use, it provides sufficient guidance for typical use.

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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