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library.discover

Automatically extracts notebook name, description, and tags from a NotebookLM URL for quick setup.

Instructions

🚀 AUTO-DISCOVERY — Automatically generate notebook metadata via NotebookLM (RECOMMENDED)

When to Use

  • User provides NotebookLM URL and wants quick/automatic setup

  • User prefers not to manually specify metadata

  • Default choice for adding notebooks

Workflow

  1. User provides NotebookLM URL

  2. Ask confirmation: "Add '[URL]' with auto-generated metadata?"

  3. Call this tool → NotebookLM generates name, description, tags

  4. Show generated metadata to user for review

Benefits

  • ✅ 30 seconds vs 5 minutes manual entry

  • ✅ Zero-friction notebook addition

  • ✅ Consistent metadata quality

  • ✅ Discovers topics user might not think of

Example

User: "Add this NotebookLM: https://notebooklm.google.com/notebook/abc123" You: "Add this notebook with auto-generated metadata?" User: "Yes" You: Call auto_discover_notebook(url="https://...") → Returns: {name: "n8n-workflow-guide", description: "...", tags: [...]}

Fallback

If auto-discovery fails (rare), use add_notebook tool for manual entry.

Visit https://notebooklm.google/ → Login (free: 100 notebooks, 50 sources each, 500k words, 50 daily queries)

  1. Click "+ New" (top right) → Upload sources (docs, knowledge)

  2. Click "Share" (top right) → Select "Anyone with the link"

  3. Click "Copy link" (bottom left) → Give this link to Claude

(Upgraded: Google AI Pro/Ultra gives 5x higher limits)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe NotebookLM notebook URL

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?

Annotations already provide readOnlyHint=false and idempotentHint=false, implying side effects. The description adds context like the workflow and fallback, but does not detail what side effects occur (e.g., if data is persisted) or clarify permissions needed. Still, it adds value beyond annotations.

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 clear sections and bullet points, but is lengthy and includes a detailed guide on obtaining the URL, which could be considered extraneous. It is front-loaded with purpose and usage, earning a 4.

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?

The description covers purpose, workflow, example, fallback, and even how to get the URL. With an output schema existing, the example response suffices. However, it lacks details on error handling or idempotency, but overall is fairly complete.

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?

The schema describes 'url' as the notebook URL (100% coverage). The description adds extra meaning via the 'How to Get a NotebookLM Share Link' section, explaining how users can obtain the URL, which goes beyond the schema.

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 auto-generates notebook metadata from a NotebookLM URL. It distinguishes itself from manual entry via the add_notebook fallback and provides a specific, actionable purpose.

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

Usage Guidelines5/5

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

The 'When to Use' section explicitly states it's the default choice for quick setup and specifies to use add_notebook if discovery fails. It also implies not to use when manual entry is preferred, providing clear context vs. alternatives.

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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