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

Annotations already declare the safe, read-only, idempotent, non-destructive profile. The description adds useful behavioral context beyond annotations: it specifies the exact return shape, explains that an empty list means nothing has been used yet, and clarifies that query_table can connect datasets on first use.

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 tool's purpose, followed by no-argument confirmation, the important query_table caveat, return shape, and empty-list semantics. Every sentence adds useful information without waste.

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 no output schema, the description provides the return object structure and the meaning of an empty list. Combined with annotations covering the safety profile and the explicit no-argument note, it is complete enough for an agent to call the tool correctly.

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 no parameters, so the baseline is 4. The description confirms 'Takes no arguments,' which is consistent with the empty schema and adds no ambiguity.

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 resource and scope: the public datasets the authenticated workspace has used so far. It differentiates itself from query_table and connect_dataset, but does not explicitly distinguish itself from the sibling list_my_datasets.

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?

Clearly explains that query_table is not limited to this list and describes how key and OAuth connections access datasets, which helps the agent avoid treating this list as a prerequisite. However, it does not explicitly say when to choose this tool over list_my_datasets or other listing tools.

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