NotebookLM MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'AI-generated' and returns a summary with keywords, but lacks details on permissions, rate limits, processing time, or error handling. For a tool with no annotation coverage, this is a significant gap in behavioral context.

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 appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections are structured clearly, though the use of markdown formatting in the description might be slightly verbose. Overall, it's efficient with minimal waste.

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 tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (implied by 'Returns'), the description is fairly complete. It explains the parameter semantics and return format adequately. However, the lack of behavioral details and usage guidelines prevents a perfect score.

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?

The description adds meaningful context beyond the input schema. The schema only specifies 'source_id' as a string with 0% description coverage, but the description clarifies it's a 'Source UUID' and explains the return values ('summary (markdown with **bold** keywords), keywords list'), which compensates well for the low schema coverage. Since there's only one parameter, the baseline is high.

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 the tool's purpose: 'Get AI-generated source summary with keyword chips.' It specifies the verb ('Get'), resource ('source summary'), and key features ('AI-generated', 'keyword chips'). However, it doesn't explicitly differentiate from sibling tools like 'source_get_content' or 'notebook_describe', which might have overlapping functionality.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'source_get_content' (which might retrieve raw content) or 'notebook_describe' (which might describe notebooks), leaving the agent to infer usage context. No exclusions or prerequisites are stated.

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