nlm_list

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

The description accurately describes the tool as a read operation that returns a list of notebooks with specific fields. With no annotations provided, the description fully discloses the behavioral traits relevant to selection and invocation.

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 a single sentence that efficiently conveys the tool's purpose. There is no wasted text, and every word earns its place.

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 has zero parameters and an output schema (not shown but referenced), the description is complete. It clearly states what the tool does, leaving no questions about its functionality.

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?

Since the tool has no parameters, the description provides all necessary semantic context. The baseline for zero-parameter tools is 4, and the description adds no conflicting or missing information.

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 action (list), the resource (NotebookLM notebooks), and the returned fields (IDs, titles, source counts). It effectively distinguishes from sibling tools like nlm_list_artifacts and nlm_list_sources, which list different resources.

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 implies use when you need an overview of notebooks, but it does not provide explicit guidance on when to use this tool versus alternatives such as nlm_list_artifacts. No exclusions or when-not guidance is given.

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