Query Research

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

With no annotations, the description carries the full burden. It discloses an authentication prerequisite and mandates verbatim presentation of the response, which is important behavioral guidance. It doesn't explicitly state read-only nature, but 'Ask a question' implies a safe query operation.

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?

Three concise, front-loaded sentences cover purpose, authentication prerequisite, and output handling. Every sentence provides necessary information with no filler.

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?

For a one-parameter query tool with no output schema, the description is complete: it defines scope, prerequisite, and how to handle the response. No additional behavioral or context cues are needed for an agent to invoke it correctly.

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 schema already fully describes the sole parameter (`question`), and the description adds only marginal context like 'research question'. Since schema coverage is 100%, the baseline of 3 is appropriate.

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 asks a research question about Commonware via NotebookLM, with a specific verb and resource. It distinguishes itself from sibling tools like search_code or search_implementation by focusing on natural-language research questions.

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?

It explicitly instructs the user to run `nlm login` first if not authenticated, giving clear prerequisite context. It does not explicitly contrast with alternatives, but the purpose and sibling set make when-to-use reasonably clear.

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