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 states the tool 'Get[s] AI-generated notebook summary,' implying it's a read-only operation that uses AI, but doesn't disclose critical traits like whether it requires authentication, has rate limits, or how it handles errors. The description adds minimal context beyond the basic action, leaving gaps in understanding the tool's behavior.

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 in a clear sentence. The additional 'Args' and 'Returns' sections are structured but slightly redundant since the output schema exists. Every sentence adds value, but it could be more concise by omitting the return details given the output schema.

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 (which handles return values), the description is mostly complete. It covers the purpose and parameter semantics well. However, it lacks behavioral details like authentication or error handling, which are important given no annotations, preventing 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 significant meaning beyond the input schema. The schema has 0% description coverage, only defining 'notebook_id' as a required string. The description specifies that 'notebook_id' is a 'Notebook UUID,' clarifying the parameter's format and purpose. Since there's only one parameter, this compensation is effective, though it doesn't detail constraints like UUID format examples.

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 notebook summary with suggested topics.' It specifies the verb ('Get'), resource ('notebook'), and output types ('summary', 'suggested_topics list'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'notebook_get' or 'notebook_query', which might also retrieve notebook information, so it doesn't reach a perfect score.

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 such as 'notebook_get' (which might fetch raw notebook data) or 'notebook_query' (which could search notebooks), leaving the agent without context for tool selection. The only implied usage is based on the purpose, but no explicit when/when-not rules are given.

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