list_datasets

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safe-read nature is covered. The description adds meaningful behavioral context: the caller-scoped listing, the compact vs. unabridged payload distinction, the larger response with include_raw, and the lack of per-file detail.

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 compact and well-organized: a one-sentence summary, an endpoint reference with practical usage hints, a sibling-tool pointer, and a parameter explanation. Every sentence earns its place; there is no filler or repetition.

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 simple read-only list tool with one optional parameter and no output schema, the description covers the essential context: scope, endpoint, how to use the results, what it does not contain, and where to go for more detail. An agent has enough to invoke it correctly and interpret its purpose.

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?

Schema description coverage is 0%, so the description must explain the parameter, and it does. It explains that include_raw returns 'each dataset's unabridged payload instead of the compact summary' and warns about the larger response, going well beyond the bare parameter name and default value.

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 opens with a specific verb and resource: 'List training datasets in the caller's account.' It clearly distinguishes this list operation from related tools by noting that it provides no per-file detail and explicitly points to get_dataset_status for file-level information.

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 gives concrete use cases: find the dataset id for upload/status/delete calls and the name for a folder_path in AI Toolkit config. It also explicitly states when to use an alternative: 'use get_dataset_status for a dataset's files.' This leaves little ambiguity for an agent deciding between siblings.

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