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add_source

Add documents, URLs, text, or YouTube videos to a NotebookLM notebook for processing and indexing to enable AI conversations with your content.

Instructions

Add a source (document, URL, text, YouTube video) to the current NotebookLM notebook.

Supported source types:

  • file: Upload a local file (PDF, DOCX, TXT, etc.)

  • url: Add a web page URL

  • text: Paste text content directly

  • youtube: Add a YouTube video URL

  • google_drive: Add a Google Drive document link

The source will be processed and indexed for use in conversations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_typeYesType of source to add
file_pathNoLocal file path (required for source_type="file")
urlNoURL (required for source_type="url", "youtube", "google_drive")
textNoText content (required for source_type="text")
titleNoOptional title/name for the source
notebook_urlNoNotebook URL. If not provided, uses the active notebook.
session_idNoSession ID to reuse an existing session
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that sources will be 'processed and indexed for use in conversations', which adds valuable behavioral context beyond the basic 'add' action. However, it doesn't mention authentication requirements, rate limits, error conditions, or what happens if the same source is added multiple times.

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 efficiently structured with a clear opening sentence stating the purpose, followed by a bulleted list of supported source types, and ending with processing behavior. Every sentence earns its place with no wasted words or redundancy.

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?

For a tool with 7 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness. It explains the tool's purpose, supported source types, and processing outcome. The main gap is the lack of output format information, but given the tool's complexity level, the description is reasonably 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?

Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds value by explaining the supported source types and their corresponding parameter requirements, but doesn't provide additional semantic context beyond what's in the schema descriptions. Baseline 3 is appropriate when schema does the heavy lifting.

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 the action ('Add a source') and the target resource ('to the current NotebookLM notebook'), with specific enumeration of supported source types. It distinguishes from sibling tools like 'delete_source' by focusing on creation rather than removal.

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 description provides clear context for when to use this tool (adding various source types to a notebook) and implicitly distinguishes it from siblings like 'create_note' or 'convert_note_to_source'. However, it doesn't explicitly state when NOT to use it or name specific alternatives for edge cases.

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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