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

No annotations are provided, so the description must cover behavioral traits. It implies the tool is a read-only list operation but does not mention permissions, rate limits, or pagination behavior. The brief description is adequate for a straightforward listing tool but lacks depth.

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 consists of two concise sentences. The first states the purpose, and the second gives usage advice. No unnecessary words; every sentence 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 no output schema and 6 parameters with full schema descriptions, the description is somewhat complete for a list tool. However, it omits details about return format (e.g., what fields are in compact vs full mode) and does not mention that results are paginated. It is minimally sufficient but could be richer.

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 6 parameters have descriptions in the schema (100% coverage), so the baseline is 3. The description adds minor value by hinting at the use of 'search' and 'compact' for low-token discovery, but does not significantly enhance understanding beyond the schema.

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 'List registered datasets and their current model tier,' which is a specific verb+resource pair. It gives a clear purpose, though it does not explicitly differentiate from sibling tools like 'list_data' or 'get_dataset_status'.

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 usage guidance: 'Use search plus compact mode for low-token discovery, then poll status or use the primary data tools once you choose a dataset.' This gives context on when to use this tool and how to proceed afterward, though it does not explicitly state when not to use it.

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