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Gerar tabela de dados

notebook_gerar_tabela

Generate a data table from NotebookLM sources in about 30 seconds, returning Markdown and saving CSV/MD files for analysis.

Instructions

Gera uma tabela de dados a partir das fontes (~30 s) e devolve em Markdown; salva .csv e .md. Os arquivos vão SEMPRE para /app/%LOCALAPPDATA%\mcp-google/saidas/notebooklm neste PC.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focoNoQue tabela criar (colunas, agrupamento). Ex.: 'descobertas: título, autor, resultado'.
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).
uso_maximoNoTrava: não gera se o uso da IA (limite atual) passar desta % somando o estimado do item. Veja notebook_uso.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.16.1

TDQS

A4/5.0
Behavior4/5

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

Adds real behavior beyond the annotations: an expected ~30 s runtime, that output is returned as Markdown, and that .csv and .md files are also written to a fixed path. This is consistent with readOnlyHint=false and destructiveHint=false (file writes, no destruction). It stops short of describing what happens on generation failure or overwrite behavior of existing files.

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

Conciseness5/5

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

Two compact sentences: what it produces first, then where the artifacts land. Nothing redundant, and the most decision-relevant facts (output format, latency, save location) are front-loaded.

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, yet the description tells the agent the return shape (Markdown) and the side files, plus the fixed output path and duration, which is enough to call it confidently. Minor gaps: no note on whether previous output files are overwritten and no behavior on long-running or failed generation.

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 each parameter is already documented in the schema, and the description adds no parameter-level detail (no clarification of 'foco' format, 'fontes' resolution, or the uso_maximo interaction beyond the schema text). Baseline 3 is appropriate when the schema does the heavy lifting.

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?

States a specific verb and resource ('Gera uma tabela de dados a partir das fontes') and names the return format (Markdown) plus side artifacts (.csv and .md). This clearly distinguishes it from the other gerar_* siblings (audio, video, slides, mapa mental, relatorio, cartoes, teste, infografico).

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

Usage Guidelines3/5

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

Implies usage context by saying the table is built from the notebook's sources and that all sources are used by default unless 'fontes' is passed, and it points to notebook_uso for the budget guard. However, it never states when to prefer this over notebook_gerar_relatorio or notebook_notas, nor any exclusion conditions.

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