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library_discover

Automatically creates notebook metadata (name, description, tags) from a NotebookLM URL, eliminating manual entry.

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.
Behavior5/5

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

The description discloses the complete workflow: it generates metadata from a URL and returns it for review, without persisting immediately. This aligns with annotations (readOnlyHint=false, idempotentHint=false) as the tool may trigger a side effect (API call) but does not save to library. No contradiction with 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 well-structured with sections, emojis, and clear formatting. It is front-loaded with the most critical information (purpose and when to use) and every sentence contributes value. Despite length, it remains efficient and scannable.

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 complexity, the description fully covers the workflow, example, fallback, and even instructions for obtaining the URL. The output schema exists, and the description explains the return values (name, description, tags). No gaps remain.

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

Parameters5/5

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

The input schema has 100% coverage with a single parameter 'url' described as 'The NotebookLM notebook URL'. The description adds significant value by providing detailed instructions on how to obtain the URL (share link steps), which goes beyond the schema definition.

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: automatically generate notebook metadata via NotebookLM. It uses a specific verb ('auto-discover') and resource ('notebook metadata'), and distinguishes from siblings like 'library_add' by positioning it as the recommended automatic setup. The example reinforces the 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 description explicitly provides 'When to Use' scenarios and a 'Fallback' instruction to use 'add_notebook' if auto-discovery fails. It outlines a clear workflow and contrasts with manual entry, giving the AI clear guidance on when to invoke this tool versus 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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