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notebooklm

notebooklm

by dfelix-net
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

generate_flashcards

Creates study flashcards from a NotebookLM notebook's sources and optional instructions, helping users review key concepts.

Instructions

Gera cartões de estudo (flashcards) no NotebookLM.

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": "generate_flashcards"
  }
}

Input Schema

NameRequiredDescriptionDefault
notebook_idYes
instructionsNoCrie cartões de estudo, em português.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing beyond purpose: no mention of cost/credits, generation latency, language behavior, or what is produced. A mutation-like generation tool with zero annotation coverage leaves the agent guessing about side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single short sentence with zero waste and the purpose front-loaded, which is structurally fine. But the brevity reflects under-specification rather than economy, so it earns only a middling score.

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

Completeness2/5

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

With no annotations, no output schema, and 0% parameter coverage, the description is too thin for a tool that triggers content generation in a crowded sibling set. It leaves the agent without the information needed to call it correctly or to choose it over alternatives.

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

Parameters2/5

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 for the undocumented parameters—and it does not. It never explains that notebook_id selects the target notebook or that 'instructions' controls generation prompt/defaults, missing the only chance to clarify the two inputs.

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 a specific verb ('Gera') and resource ('cartões de estudo (flashcards)') in a named environment (NotebookLM), so the operation is unambiguous. However, it does nothing to separate itself from siblings such as generate_quiz or generate_mind_map, which share the same 'generate X from a notebook' shape.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus generate_quiz, generate_mind_map, or generate_summary_report, nor any prerequisite or exclusion. The agent must infer usage purely from the name.

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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