Skip to main content
Glama

save_auth_tokens

Save NotebookLM authentication tokens as fallback when automated CLI login fails. Provide cookies from Chrome DevTools to authenticate.

Instructions

Save NotebookLM cookies (FALLBACK method - try nlm login first!).

IMPORTANT FOR AI ASSISTANTS:

  • First, run nlm login via Bash/terminal (automated, preferred)

  • Only use this tool if the automated CLI fails

Args: cookies: Cookie header from Chrome DevTools (only needed if CLI fails) csrf_token: Deprecated - auto-extracted session_id: Deprecated - auto-extracted request_body: Optional - contains CSRF if extracting manually request_url: Optional - contains session ID if extracting manually

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cookiesYes
csrf_tokenNo
session_idNo
request_urlNo
request_bodyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations so description bears full burden. Mentions deprecations and auto-extraction but lacks details on persistence, side effects, or security implications. Adequate but not thorough.

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?

Well-structured with clear hierarchy (important note, bullet list). Slightly verbose but front-loads critical guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers usage context well but lacks completeness on outcomes, error scenarios, or what 'save' entails. Output schema exists but not referenced.

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?

With 0% schema coverage, description fully compensates by explaining each parameter's purpose, when needed, and status (deprecated/optional). Adds meaning beyond schema types.

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?

Clearly states it saves cookies as a fallback method for authentication, with explicit preference for 'nlm login'. Distinguishes itself from sibling tools by indicating primary method.

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 explicit when-to-use ('only if CLI fails') and when-not ('try nlm login first'), along with deprecated parameter notes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/whmathews15/notebooklm-mcp-cli'

If you have feedback or need assistance with the MCP directory API, please join our Discord server