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Gerar cartões didáticos

notebook_gerar_cartoes

Generate study flashcards from a NotebookLM notebook, returning front/back cards and saving Markdown and CSV files for Anki import.

Instructions

Gera cartões didáticos (flashcards, ~30 s) e devolve frente/verso; salva .md e .csv (importável no Anki). Os arquivos vão SEMPRE para /app/%LOCALAPPDATA%\mcp-google/saidas/notebooklm neste PC.

Input Schema

NameRequiredDescriptionDefault
focoNoTema dos cartões (ex.: 'termos técnicos na frente, definição atrás').
fontesNoUsar só estas fontes (títulos ou ids). Padrão: todas.
idiomaNoIdioma do resultado (ex.: pt-BR, en, es, fr, ou o nome como aparece no NotebookLM). Padrão: o do notebook (português).
notebookYesId, URL ou título do notebook (o título pode ser parcial).
quantidadeNomenos, padrao ou mais cartões.
uso_maximoNoTrava: não gera se o uso da IA (limite atual) passar desta % somando o estimado do item. Veja notebook_uso.
dificuldadeNofacil (Fácil), medio (Médio (padrão)), dificil (Difícil)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.16.1

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already signal a non-read-only, non-destructive, open-world write. The description adds real value beyond them: it discloses the exact output location (/app/%LOCALAPPDATA%\mcp-google/saidas/notebooklm), that files are saved as both .md and .csv (Anki-importable), and that front/back is returned inline. These are concrete side-effect details the annotations cannot convey.

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?

Two sentences with the core action and side effects front-loaded; nothing is padded. The path string is somewhat noisy but it carries necessary information about where files land.

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?

For a generation tool with 100% schema coverage and no output schema, the description covers what is produced (front/back), where files go, and import compatibility. Only the sibling routing and any permission/auth prerequisites are left implicit.

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 all seven parameters (foco, fontes, idioma, notebook, quantidade, uso_maximo, dificuldade) are already documented in the schema. The description adds no parameter-level meaning, so the baseline 3 is appropriate.

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?

States a specific verb (gera) and resource (cartões didáticos/flashcards), plus what it produces (frente/verso) and side effects (.md/.csv files). The resource is distinct enough from siblings like notebook_gerar_teste or notebook_gerar_relatorio that an agent can pick it, though it does not explicitly name the alternatives.

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 explicit when-to-use guidance or comparison to alternatives such as notebook_gerar_teste or notebook_gerar_relatorio. The usage is inferable from the resource name, but the description leaves selection entirely to inference.

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