list-experiments-for-dataset

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It explains the concept of experiments but does not mention read-only nature, authentication needs, rate limits, or any side effects. The description provides minimal behavioral context.

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 reasonably well-structured, including a brief conceptual note and an example usage with expected return. It is not overly verbose, though some of the explanatory context (e.g., 'Experiments are collections...') could be shortened without losing value.

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 lack of annotations, output schema, and incomplete parameter explanations, the description is insufficient. It omits details on how to use the 'dataset_name' parameter, the effect of 'limit', pagination, ordering, and any error conditions, leaving significant gaps for an AI agent.

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 3 parameters with 0% description coverage. The description does not explain the 'dataset_name' or 'limit' parameters, only indirectly referencing 'dataset_id' via the example. It fails to add meaning beyond the schema's type and constraints.

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 'Get a list of all the experiments run on a given dataset,' specifying the verb and resource. It includes an example usage, but does not differentiate from the very similar sibling tool 'get-dataset-experiments', which reduces clarity.

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 an example usage with a dataset ID, implying how to use the tool, but lacks explicit guidance on when to prefer this tool over alternatives like 'get-dataset-experiments' or 'get-experiment-by-id'. No when-not or exclusion criteria are given.

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