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roomi-fields

notebooklm-mcp

by roomi-fields
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

content_generate

Generate audio overviews, videos, presentations, reports, infographics, or data tables from your NotebookLM sources with customizable language and style options.

Instructions

Generate content from your NotebookLM sources.

Supported content types:

  • audio_overview: Audio podcast/overview (Deep Dive conversation with two AI hosts)

  • video: Video summary that visually explains main topics (brief or explainer format)

  • presentation: Slides/presentation with AI-generated content and images

  • report: Briefing document (2,000-3,000 words) summarizing key findings, exportable as PDF/DOCX

  • infographic: Visual infographic in horizontal (16:9) or vertical (9:16) format

  • data_table: Structured table organizing key information (exportable as CSV/Excel)

Language support: All content types support 80+ languages via the language parameter.

Video styles: Video content supports 6 visual styles via the video_style parameter: classroom, documentary, animated, corporate, cinematic, minimalist.

These content types use real NotebookLM Studio UI buttons or the generic ContentGenerator architecture that navigates the Studio panel and falls back to chat-based generation.

NOTE: Other content types (faq, study_guide, timeline, table_of_contents) are NOT currently implemented. For document-style content, use the ask_question tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
content_typeYesType of content to generate: audio_overview (podcast), video (brief or explainer), presentation (slides), report (briefing doc 2,000-3,000 words, PDF/DOCX export), infographic (horizontal 16:9 or vertical 9:16), or data_table (CSV/Excel export)
custom_instructionsNoOptional instructions to customize the generated content
languageNoLanguage for the generated content (e.g., "French", "Spanish", "Japanese"). NotebookLM supports 80+ languages.
video_styleNoVisual style for video content (only valid for content_type="video"). Powered by Nano Banana AI.
notebook_urlNoNotebook URL. If not provided, uses the active notebook.
session_idNoSession ID to reuse an existing session

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successYesWhether the tool call succeeded.
dataNoThe tool payload on success. The exact shape depends on the tool.
errorNoHuman-readable error message, present only when success is false.

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

Adds rich behavioral context beyond annotations: mentions fallback to chat-based generation, supported languages, video styles, and notes unimplemented types. No contradictions with annotations.

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?

Well-structured with bullet points and sections. Front-loaded with main purpose. Some redundancy but every sentence adds value. Could be slightly more concise, but overall efficient.

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

Completeness5/5

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

Comprehensive for a complex generation tool with 6 parameters and output schema. Covers content types, language, video styles, unsupported types, and fallback behavior. Output schema exists, so return values need not be described.

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 coverage is 100% (baseline 3). Description adds extra meaning: details on content_type enum values, video_style options with 'Powered by Nano Banana AI', language support for 80+ languages. Improves usability beyond schema.

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?

Clearly states 'Generate content from your NotebookLM sources.' Lists supported content types with specific descriptions, distinguishing from sibling tool 'ask_question' for document-style content.

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?

Explicitly tells when to use (for generating content) and when not (faq, study_guide, etc.), directing users to use ask_question for document-style content. Provides clear context for each content type.

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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