list_dataset_topics

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only indicates that the tool lists topics, implying a read operation, but says nothing about pagination, sorting behavior, authentication requirements, or what the returned topics look like.

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 with no filler, making it easy to parse. However, it is so minimal that it borders on restating the tool name, and it omits necessary details for a tool with four parameters.

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?

The tool has 4 parameters, no schema descriptions, no annotations, and no output schema, yet the description provides no operational context. It fails to explain the required 'ref' parameter, the behavior of search/sort/page_size, or the expected return value.

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?

All four parameters have empty descriptions in the schema, and the description adds no information about ref, search, sort_by, or page_size. An agent has no way to know what values are valid or what the parameters control.

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 uses a clear verb 'List' with a specific resource 'dataset discussion topics', making the tool's purpose immediately understandable. The word 'dataset' distinguishes it from sibling tools like list_kernel_topics and list_competition_topics, though it does not explicitly name those alternatives.

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?

There is no guidance about when to use this tool versus alternatives such as show_dataset_topic or list_kernel_topics. The description simply states what the tool does, with no context on selection criteria or situations where another tool would be more appropriate.

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