list_datasets

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

There are no annotations, so the description carries the burden of behavioral disclosure. 'List' implies a read-only operation and the description adds helpful scoping context (local machine, defaults), but it does not explicitly state that there are no side effects or describe what the returned list contains.

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 a single front-loaded sentence with no filler or redundant phrasing. The semicolon clause is somewhat awkward but carries useful scope information.

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 zero-parameter list tool, the description is mostly sufficient, noting machine scope and defaults. However, with no output schema and no annotations, it does not explicitly explain the return shape or confirm that the operation is purely read-only.

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 has zero parameters and 100% schema description coverage, so there is nothing meaningful to add. The baseline of 4 applies since no parameter documentation 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?

The description uses a specific verb ('List') and resource ('datasets (memory scopes)'), and clarifies scope with 'on this machine' plus the default project and user-global dataset. It is clear enough, though the post-semicolon clause is slightly ambiguous and does not explicitly distinguish it from sibling tools.

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?

No guidance is provided about when to call this tool versus siblings like recall, global_context, or session tools. The usage context is only implied by the word 'List', not explicitly stated.

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