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source_add

Add files, URLs, text, or YouTube videos to a NotebookLM notebook, making them available for indexed Q&A in conversations.

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

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.
Behavior3/5

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

The description adds that the source will be 'processed and indexed for use in conversations,' which provides some behavioral context beyond the annotations (readOnlyHint=false, idempotentHint=false, openWorldHint=true). However, it omits details about side effects like duplication on repeated calls, authentication needs, or rate limits. The description does not contradict annotations.

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 well-structured, using a bullet list for source types. Every sentence adds value, and it is appropriately front-loaded with the core purpose.

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 tool's moderate complexity (7 parameters, 1 required), the description provides sufficient context: it states the processing and indexing behavior. Since an output schema exists, return values need not be explained. It is well-rounded, though it could mention input size limits or processing delays.

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 covers all seven parameters with descriptions, achieving 100% coverage. The description lists supported source types but does not add deeper meaning beyond the schema (e.g., format constraints, size limits). Per the guidelines, baseline 3 is appropriate when schema coverage is high.

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 tool's purpose: 'Add a source (document, URL, text, YouTube video) to the current NotebookLM notebook.' It enumerates five specific source types, making the action concrete and distinguishing it from sibling tools like source_delete.

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?

The description explains what the tool does but does not provide explicit guidance on when to use it versus alternatives. There is no mention of when not to use it or which sibling tools might be more appropriate for similar tasks (e.g., content_generate for creating content). The context of processing and indexing is helpful but lacks exclusions.

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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