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save_chat_to_note

Extract and save conversation messages from NotebookLM chats into formatted notes with timestamps and attribution for research preservation and discussion summaries.

Instructions

Save the current NotebookLM chat/discussion to a note.

This tool extracts all messages from the current conversation (both user questions and NotebookLM AI responses) and saves them as a formatted note in the Studio panel.

Use this to:

  • Preserve important research conversations

  • Create a summary of your discussion with NotebookLM

  • Save chat history before starting a new topic

The note will include timestamps and message attribution (User/NotebookLM).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoCustom title for the note (default: "Chat Summary")
notebook_urlNoNotebook URL. If not provided, uses the active notebook.
session_idNoSession ID to reuse an existing session
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that the tool extracts ALL messages, includes timestamps and attribution, and saves to the Studio panel, which are useful behavioral traits. However, it doesn't mention potential limitations like maximum chat length, whether it overwrites existing notes, or what happens if no active notebook exists.

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 a clear purpose statement followed by bulleted use cases and a final behavioral detail. Every sentence adds value with no redundancy or wasted words, making it efficient and front-loaded.

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?

For a tool with 3 parameters, 100% schema coverage, and no output schema, the description provides good context about what the tool does and when to use it. However, without annotations or output schema, it could benefit from more detail about the resulting note format or any limitations.

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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3 for high schema coverage without compensating value.

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 specific action ('extracts all messages... and saves them as a formatted note') and resource ('current NotebookLM chat/discussion'), distinguishing it from sibling tools like create_note or convert_note_to_source by focusing specifically on chat conversation preservation.

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

Usage Guidelines4/5

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

The description provides three explicit use cases ('Preserve important research conversations', 'Create a summary of your discussion', 'Save chat history before starting a new topic'), giving clear context for when to use this tool. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings.

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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