NotebookLM MCP Server

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quiz_create

Create quizzes from NotebookLM content by specifying sources, question count, and difficulty level to assess knowledge retention.

Instructions

Generate quiz. Requires confirm=True after user approval.

Args: notebook_id: Notebook UUID source_ids: Source IDs (default: all) question_count: Number of questions (default: 2) difficulty: Difficulty level (default: medium) confirm: Must be True after user approval

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": {
      "notebook_id": "<notebook_id>"
    },
    "name": "quiz_create"
  }
}

Input Schema

Name	Required	Default
`notebook_id`	Yes
`source_ids`	No
`question_count`	No
`difficulty`	No	medium
`confirm`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.15Feb 9, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the confirm requirement, which is a behavioral trait, but lacks details on permissions, rate limits, what happens on failure, or the quiz generation process. It's adequate but has gaps for a mutation tool.

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 appropriately sized with a front-loaded main sentence and a structured Args section. Every sentence adds value, though the Args formatting could be slightly more integrated for better flow.

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?

Given 5 parameters with 0% schema coverage and no annotations, the description does well by explaining all parameters and including a usage guideline. An output schema exists, so return values needn't be described. It's mostly complete but could benefit from more behavioral context for a mutation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds meaning for all 5 parameters by explaining their purposes (e.g., 'Notebook UUID', 'Source IDs (default: all)'), which goes beyond the bare schema. However, it doesn't detail formats like UUID structure or difficulty enum values, keeping it from a perfect score.

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 clearly states the verb 'Generate' and resource 'quiz', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'flashcards_create' or 'report_create' which might also generate educational content, so it misses full sibling differentiation.

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 provides explicit usage guidance with 'Requires confirm=True after user approval', indicating when to use this tool (after user approval) and a prerequisite condition. This is clear and actionable for an AI agent.

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