content.generate

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

Annotations already indicate readOnlyHint=false and idempotentHint=false, so the agent knows this is a state-changing operation. The description adds context about using real UI buttons or generic ContentGenerator architecture and fallback to chat-based generation, but does not detail side effects or permission requirements beyond what annotations imply.

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

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

The description is front-loaded with the main purpose and then structured sequentially: content types, language support, video styles, and a warning about unimplemented types. While some implementation details (e.g., 'uses real NotebookLM Studio UI buttons') could be trimmed, the overall structure is logical and each sentence adds value.

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

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

Given the complexity (6 content types, 2 enums, optional parameters) and the presence of an output schema, the description covers all necessary aspects: what each type produces, language support, video style options, and notes on unimplemented types. It also clarifies that the output format varies by type. No critical gaps are evident.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3. The description adds significant meaning by explaining what each content_type produces (e.g., 'audio_overview: Audio podcast/overview (Deep Dive conversation with two AI hosts)') and detailing video_style enum values with visual style descriptions and 'powered by Nano Banana AI', going beyond the schema's simple names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Generate content from your NotebookLM sources' and enumerates all supported content types with brief explanations. It distinguishes itself from siblings by explicitly noting unimplemented types and directing to ask_question tool for those, ensuring the agent understands its specific domain.

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

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

The description provides clear context on when to use this tool (for generating supported content types) and explicitly mentions which types are NOT implemented (faq, study_guide, timeline, table_of_contents) with a fallback suggestion to use ask_question. However, it lacks explicit guidance on when to prefer this over other parallel tools, though few exist.

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