notebooklm-mcp-cli

Save Auth Tokens

save_auth_tokens

Save NotebookLM cookies to authenticate when automated login fails, ensuring continued access to notebooks and content.

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

Input Schema

TableJSON Schema

Name	Required	Description
`cookies`	Yes	Cookie header from Chrome DevTools (only needed if CLI fails)
`csrf_token`	No	Deprecated - auto-extracted
`session_id`	No	Deprecated - auto-extracted
`request_url`	No	Optional - contains session ID if extracting manually
`request_body`	No	Optional - contains CSRF if extracting manually

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.11.5Sep 18, 2026

TDQS

A3.6/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It explains the fallback workflow but does not describe side effects, storage behavior, whether existing auth is overwritten, or what the tool returns after saving tokens.

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?

The description is short and front-loaded with the key fallback message. The bullet list repeats the 'try nlm login first' idea slightly, but overall it is efficient and well-structured for an AI agent.

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?

The description provides enough routing context for the agent to know when to use the tool, and the schema covers parameters and output. However, without annotations, the lack of behavioral and side-effect details leaves some context gaps for a sensitive auth-related operation.

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 parameter descriptions already document each field well. The main description adds little parameter-specific meaning beyond identifying cookies as the required input, which is a reasonable baseline given the high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('Save') and resource ('NotebookLM cookies'), and labels the tool as a FALLBACK method for authentication. It clearly conveys the purpose, though it does not explicitly differentiate from the sibling `refresh_auth` tool.

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 gives explicit guidance: run `nlm login` first via Bash/terminal, and only use this tool if the automated CLI fails. This is clear, actionable, and leaves little room for an agent to misuse the tool.

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