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library.add

Add a notebook to your library by manually providing its URL, name, description, and topics. Use when auto-discovery fails or you need specific metadata.

Instructions

📝 MANUAL ENTRY — Add notebook with manually specified metadata (use auto_discover_notebook instead)

When to Use

  • Auto-discovery failed or unavailable

  • User has specific metadata requirements

  • User prefers manual control

Conversation Workflow (Mandatory)

When the user says: "I have a NotebookLM with X"

FIRST: Try auto_discover_notebook for faster setup ONLY IF user refuses auto-discovery or it fails:

  1. Ask URL: "What is the NotebookLM URL?"

  2. Ask content: "What knowledge is inside?" (1–2 sentences)

  3. Ask topics: "Which topics does it cover?" (3–5)

  4. Ask use cases: "When should we consult it?"

  5. Propose metadata and confirm:

    • Name: [suggested]

    • Description: [from user]

    • Topics: [list]

    • Use cases: [list] "Add it to your library now?"

  6. Only after explicit "Yes" → call this tool

Rules

  • Do not add without user permission

  • Prefer auto_discover_notebook when possible

  • Do not guess metadata — ask concisely

  • Confirm summary before calling the tool

Example

User: "I have a notebook with n8n docs" You: "Want me to auto-generate the metadata?" (offer auto_discover_notebook first) User: "No, I'll specify it myself" You: Ask URL → content → topics → use cases; propose summary User: "Yes" You: Call add_notebook

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
nameYesDisplay name for the notebook (e.g., 'n8n Documentation')
descriptionYesWhat knowledge/content is in this notebook
topicsYesTopics covered in this notebook
content_typesNoTypes of content (e.g., ['documentation', 'examples', 'best practices'])
use_casesNoWhen should Claude use this notebook (e.g., ['Implementing n8n workflows'])
tagsNoOptional tags for organization

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?

Annotations indicate mutation (readOnlyHint=false). The description adds behavioral context: requires user permission, manual metadata collection, and that it adds a notebook to the library. No contradictions.

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

Conciseness3/5

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

While structured with headers and examples, the description is quite lengthy and includes extraneous information like how to get a NotebookLM share link, reducing conciseness.

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 (7 parameters, required fields, mutation activity), the description is very comprehensive, covering workflow, rules, example, and even external steps. An output schema exists, so return value explanation is not needed.

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 coverage is 100% with descriptions. The description provides an example but does not add substantial meaning beyond the schema. Baseline 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 'Add notebook with manually specified metadata' and contrasts with auto_discover_notebook, clearly distinguishing the tool's purpose from its sibling.

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?

Provides detailed when-to-use conditions (auto-discovery failed, specific metadata, manual control), explicit alternative (auto_discover_notebook), and a mandatory conversational workflow with step-by-step instructions.

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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