notebook_describe

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 generates an AI summary, implying it's a read operation, but doesn't cover critical aspects like whether it requires specific permissions, has rate limits, or details about the AI model used. The description adds minimal behavioral context beyond the basic purpose.

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 well-structured and concise, with a clear purpose statement followed by Args and Returns sections. Every sentence earns its place by providing essential information without redundancy. It could be slightly more front-loaded by integrating the Args/Returns into the main description, but it's efficient overall.

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 moderate complexity (AI generation), no annotations, and the presence of an output schema (implied by the Returns section), the description is reasonably complete. It covers the purpose, parameter meaning, and return values, though it lacks behavioral details like error handling or performance characteristics. The output schema reduces the need for extensive return value explanation.

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 some parameter semantics by specifying 'notebook_id: Notebook UUID' in the Args section, which clarifies the parameter's purpose beyond the schema's type definition. However, with 0% schema description coverage and only 1 parameter, this provides basic but not comprehensive value. The baseline is appropriate given the low parameter count.

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 type ('summary with suggested topics'). However, it doesn't explicitly differentiate from sibling tools like 'notebook_get' or 'notebook_query', which prevents 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 like 'notebook_get' (which might retrieve raw content) or 'notebook_query' (which might search notebooks), leaving the agent without context for tool selection. Usage is implied only by the purpose statement.

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