get-dataset-list

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

No annotations are provided, so the description carries main responsibility. It discloses the important behavior that unnamed datasets are excluded by default and that actor-run outputs are unnamed, but it does not describe the response format, pagination behavior, or potential errors. This is partial transparency.

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?

Two sentences with no wasted words. The first states the action and the use case, and the second provides a critical usage hint. Every part 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?

The four parameters are fully documented in the schema, and the description covers the main use case and a key filtering nuance. The lack of an output schema is not critical for a basic list operation, though a brief note on return value shape would make it complete.

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 100%, so the baseline is 3. The description restates the unnamed flag's purpose but does not add meaning beyond the schema, except contextual rationale for why the flag is needed.

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?

States a specific action 'List datasets on the account' with a concrete use case: locating a run's output dataset when its ID was lost. This clearly distinguishes it from siblings like get-dataset-items, which targets items within a dataset.

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?

Provides clear context: use this when you need to find a run's output dataset, and explicitly signals that unnamed=true is required for that purpose. It does not mention alternative tools to avoid, so it misses the full 'when-not-to-use' guidance.

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