notebooklm-query

name	notebooklm-query
description	Access NotebookLM to ask questions and retrieve information. Specify a NotebookLM URL and query.
argument-hint	<NotebookLM URL> <question or research topic>
allowed-tools	["mcp__claude-in-chrome__tabs_context_mcp","mcp__claude-in-chrome__tabs_create_mcp","mcp__claude-in-chrome__navigate","mcp__claude-in-chrome__computer","mcp__claude-in-chrome__read_page","mcp__claude-in-chrome__find","mcp__claude-in-chrome__form_input","mcp__claude-in-chrome__javascript_tool","mcp__claude-in-chrome__get_page_text","Read","Glob","Grep","AskUserQuestion"]

Access a NotebookLM notebook, ask questions via chat, and retrieve information.

Input

The user provides:

• NotebookLM URL (e.g., https://notebooklm.google.com/notebook/xxxx)
• Question or research topic (one or more)

If arguments are missing, use AskUserQuestion to clarify.

Instructions

Step 1: Get Browser Context

1. Use tabs_context_mcp to get tab info
2. If the specified URL is already open in a tab, use that tab
3. Otherwise, use tabs_create_mcp to create a new tab and navigate to the URL
4. Wait 5-10 seconds for the page to fully load

Step 2: Verify Page Readiness

1. Take a screenshot to check the page state
2. Confirm the NotebookLM chat input field is visible
3. If login is required, notify the user and wait

Step 3: Send Question

Craft an effective question from the user's research topic and send it to NotebookLM.

1. Use find to locate the chat input field
2. Use form_input to type the question text
3. Use find to locate the "Send" button and click the one near the chat input
   • Note: There may be multiple send buttons (source search and chat). Use the one closest to the chat input.
4. Wait 15-25 seconds (response generation takes time)

Step 4: Retrieve Response

1. Take a screenshot to confirm the response is displayed
2. Use javascript_tool to extract the response text:

   const chatArea = document.querySelector('[role="log"], [class*="chat"], [class*="conversation"]');
   if (chatArea) {
     const text = chatArea.innerText;
     const lastQ = text.lastIndexOf('<partial question text>');
     if (lastQ >= 0) text.substring(lastQ, lastQ + 5000);
   }
   

3. For long responses, adjust offset and retrieve in multiple passes
4. If an expand button (▼) appears at the end of the response, click it before re-extracting

Step 5: Follow-up Questions (if needed)

• For broad research topics, send follow-up questions based on initial responses
• Repeat Steps 3-4
• Maximum 5 questions per skill execution

Step 6: Organize Results

Present the retrieved information in this format:

## NotebookLM Research Results

### Source
- Notebook: [Title](URL)
- Sources: N items

### Q1: <question>
<response summary>

### Q2: <question>
<response summary>

### Key Takeaways
- Point 1
- Point 2
- ...

Tips

• NotebookLM responses can be long; use javascript_tool with varying offsets for multiple retrieval passes
• Extract code examples and JSON accurately
• If NotebookLM is still generating (loading indicator), add extra wait time
• If chat history is long, the input field may scroll off-screen; scroll down before interacting

Error Handling

• Tab closed: Re-fetch with tabs_context_mcp and re-access in a new tab
• Login required: Ask the user to log in manually, then resume
• No response generated: Wait an additional 15 seconds; if still empty, retry submission (max 2 retries)
• Send button not found: Use read_page to list interactive elements and identify the button