get-dataset-experiments

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

No annotations are provided, so the description must carry the burden. It only states it lists experiments and returns an array of objects, but lacks details on authentication, rate limits, side effects, or how dataset_id vs dataset_name are handled. Behavioral traits are minimally disclosed.

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 and front-loaded with the purpose. It includes an example and expected return without unnecessary details, earning its sentences.

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?

The tool has 3 parameters and no output schema, yet the description omits parameter semantics and usage context. The existence of a sibling with similar name adds confusion. More detail is needed for complete understanding.

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?

Schema description coverage is 0%, so the description should compensate. It barely mentions parameters; only the example implies dataset_id use. No explanations for dataset_id, dataset_name, or limit, leaving the agent to guess meaning and usage.

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 experiments run on a dataset, with an example. However, it does not differentiate from the sibling tool 'list-experiments-for-dataset', which appears to have the same purpose, so clarity is high but not perfect.

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 on when to use this tool versus alternatives. The sibling 'list-experiments-for-dataset' exists but the description does not mention it or any decision criteria. The example is generic and does not clarify usage context.

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