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generate_content

Generate audio, video, presentations, reports, infographics, or data tables from NotebookLM sources in 80+ languages with customizable styles.

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
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the implementation approach (using NotebookLM Studio UI buttons or ContentGenerator architecture with fallback to chat-based generation), which adds useful context. However, it doesn't disclose important behavioral aspects like whether this is a read-only or mutation operation, what permissions are needed, rate limits, or what the output format looks like beyond basic export formats mentioned.

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?

The description is appropriately front-loaded with the core purpose, but contains some redundancy (language support mentioned twice, video styles explained in both description and schema). The NOTE section about unimplemented content types and alternative tool could be more integrated. Overall, it's reasonably structured but could be more concise by eliminating repetition.

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

Completeness3/5

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

For a tool with 6 parameters, no annotations, and no output schema, the description provides adequate context about what content types are available and implementation details. However, it doesn't explain what the tool actually returns (only mentions export formats), doesn't cover error conditions, and doesn't provide examples of successful use. Given the complexity and lack of structured output information, more completeness would be helpful.

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 the schema already documents all 6 parameters thoroughly with descriptions and enums. The description adds some value by explaining language support (80+ languages) and video styles in more detail than the schema, but doesn't provide significant additional parameter semantics beyond what's already in the well-documented schema.

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 clearly states the tool generates content from NotebookLM sources and lists specific content types with brief explanations. It distinguishes this tool from 'ask_question' by explicitly stating that tool should be used for document-style content instead. However, it doesn't fully differentiate from all sibling tools like 'download_content' or 'list_content'.

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?

The description provides clear context about when to use this tool (for generating various content types from sources) and explicitly states when NOT to use it (for faq, study_guide, timeline, table_of_contents which are not implemented). It also provides an alternative tool ('ask_question') for document-style content. However, it doesn't provide guidance on when to choose between this tool and other content-related siblings like 'download_content'.

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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