list_datasets

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

Beyond the readOnlyHint annotation, the description discloses a 5-minute result cache, which materially affects how an agent should treat freshness. It also explains the import_mode values (SPICE cached vs DIRECT_QUERY live), adding real semantic context. Minor gap: no disclosure of pagination or result-size limits for a whole-account listing.

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?

Front-loaded with the action and resource, then a compact bulleted field list, then usage and caching notes. The field enumeration is slightly redundant given an output schema exists, but it is short and readable.

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?

With an output schema present, the description didn't need to restate return fields, yet it still explains import_mode semantics and caching, which the schema cannot convey. It is essentially complete for a no-argument discovery tool, missing only guidance on volume/pagination.

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 tool takes zero parameters, so the baseline of 4 applies. There are no parameter semantics to document and the description appropriately spends no space on them.

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 opening sentence states a specific verb and resource ('List all QuickSight datasets') and enumerates the returned fields. It does not, however, differentiate itself from the closely related sibling search_datasets (the filtered variant), leaving an agent to infer which listing tool to pick.

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?

It gives clear downstream context: 'Use this to discover datasets before calling get_dataset_sql or update_dataset_sql,' which tells the agent where this fits in a workflow. It stops short of stating when NOT to use it or naming search_datasets as the filtered alternative.

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