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Gerar Resumo em Vídeo

notebook_gerar_video

Generate a narrated video summary with slides or illustrations from a notebook and save it as an MP4 file.

Instructions

Gera um Resumo em Vídeo (vídeo narrado com slides/ilustrações) e salva o .mp4. Leva de ~5 a 20 min (no Google) e gasta ~9-11% do limite de uso. No Claude Desktop (limite de ~50 s por chamada) a resposta costuma vir 'ainda gerando': chame notebook_estudio_status até baixar. No Claude Code a chamada já espera. Os arquivos vão SEMPRE para /app/%LOCALAPPDATA%\mcp-google/saidas/notebooklm neste PC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focoNoAssunto/foco do vídeo.
estiloNoSó no explicativo: automatico (Seleção automática), personalizado (Personalizado), classico (Clássico), quadro_branco (Quadro branco), kawaii (Kawaii), anime (Anime), aquarela (Aquarela), impressao_retro (Impressão retrô), retro (Retrô), dobradura (Dobradura).
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).
formatoNocurto (Curta), explicativo (Vídeo explicativo). Padrão: curto.
notebookYesId, URL ou título do notebook (o título pode ser parcial).
uso_maximoNoTrava: não gera se o uso da IA (limite atual) passar desta % somando o estimado do item. Veja notebook_uso.
estilo_personalizadoNoSó no explicativo: descreva o estilo visual (ex.: 'ilustração de livro infantil').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.16.1

TDQS

A4.1/5.0
Behavior5/5

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

Annotations only say it is a non-destructive, open-world write; the description adds the expensive, long-running, sometimes-asynchronous nature and the exact output location. The async-vs-blocking distinction per client is precisely the kind of behavior annotations cannot express.

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?

Four dense sentences, front-loaded with what the tool produces before cost, latency, and polling behavior. Each sentence carries information, though the absolute file-path sentence is long and only marginally actionable given the tool reports the path anyway.

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?

No output schema exists, and the description compensates by explaining that the .mp4 is saved to disk and that the immediate response may be 'ainda gerando' rather than the finished artifact. It does not describe the shape of the returned identifier/status object, leaving a small gap for an agent parsing the response.

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%, including enums for estilo and formato, so the schema already carries the parameter burden. The description adds nothing about parameters beyond the implicit link to notebook_uso via the usage budget, which is already stated in the schema. Baseline 3.

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 and resource: generates a 'Resumo em Vídeo (vídeo narrado com slides/ilustrações)' and saves the .mp4. Clear enough to act on, but it never contrasts itself with the adjacent siblings notebook_gerar_slides or flow_gerar_video, so the agent must infer which video generator to pick.

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

Usage Guidelines4/5

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

Gives concrete operational context: ~5-20 min runtime on Google, ~9-11% of the usage limit, and an explicit platform-conditional follow-up ('no Claude Desktop... chame notebook_estudio_status até baixar'). That effectively tells the agent when extra polling is required. It stops short of saying when to prefer this over the other generation tools.

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