datasets_list

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

No annotations are provided, so the description carries the full burden. It only states it lists datasets and returns names, with no mention of side effects, permissions, or behavioral traits beyond a simple read operation. This is minimal for 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?

The description is concise (3 lines), front-loaded with the core purpose, and includes parameter documentation. It is efficient but could be slightly more structured with a brief explanation of datasets.

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 the tool's simplicity (one optional parameter, no output schema), the description adequately covers purpose, parameter, and return type. It is sufficiently complete for this low-complexity tool, though it lacks sibling differentiation.

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 schema has 0% description coverage, but the description adds meaningful context for the single parameter (model_name), explaining that it defaults to the current model. This compensates for the schema gap.

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 all datasets in a model, using a specific verb and resource. However, it does not explicitly differentiate from sibling list tools like docs_list or solutions_list, though the resource is distinct.

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 provides context that datasets represent solution data for evaluation or visualization, but it does not specify when to use this tool over alternatives or when not to use it. No explicit usage guidance is given.

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