NotebookLM MCP Server

by Robiton

Overview Schema Source Related Servers Discussions Admin

Python

Remote

save_auth_tokens

Save NotebookLM authentication tokens using cookies from Chrome DevTools when the automated CLI login fails.

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "cookies": "<cookies>"
    },
    "name": "save_auth_tokens"
  }
}

Input Schema

Name	Required	Description	Default
`cookies`	Yes
`csrf_token`	No
`session_id`	No
`request_body`	No
`request_url`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.8May 28, 2026

TDQS

A4.5/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses fallback nature, deprecation, and auto-extraction but does not detail side effects (e.g., file writing, overwriting) or permissions needed, leaving some behavioral gaps.

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 well-structured with bullet points and front-loaded purpose. Some redundancy (e.g., 'IMPORTANT FOR AI ASSISTANTS') but no wasted words.

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 output schema exists and annotations are absent, the description covers all parameters, usage logic, and alternatives, making it complete for a fallback authentication tool.

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?

Schema coverage is 0%, but the description adds detailed meaning: cookies source, deprecation, auto-extraction, and manual extraction conditions. This fully compensates for the missing schema descriptions.

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 it saves NotebookLM cookies as a fallback method. It distinguishes from the preferred 'nlm login' command, making the purpose and context explicit.

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?

Explicitly instructs to try 'nlm login' first and only use this tool if CLI fails. Also notes deprecated parameters, guiding proper usage.

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