list_eval_datasets

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

No annotations are provided, so the description carries full behavioral burden. It indicates this is a read-only GET operation and describes the response fields. While it does not mention auth or rate limits, the simplicity of the operation makes this adequate.

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 two sentences: first gives purpose and endpoint, second details return fields and context. No extraneous content; every sentence serves a purpose.

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?

Given no output schema, the description sufficiently explains the return value structure and the semantic meaning of golden datasets. It is complete for a parameterless list tool.

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 schema coverage is 100%. The description adds value by explaining the returned data (name, description, item count, frozen state) and the concept of a golden dataset, so it exceeds the baseline 3.

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 clearly states the tool lists golden datasets using a GET endpoint, specifies the returned fields (name, description, item count, frozen state), and provides context explaining what a golden dataset is. This differentiates it from sibling tools like list_eval_runs or list_eval_criteria.

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 implicitly defines when to use this tool (to view all golden datasets) and hints at related actions (run_eval_dataset). However, it lacks explicit when-not or alternative usage instructions, which would elevate it to a 5.

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