list-experiments-for-dataset

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

No annotations provided, so description must cover behavioral traits. It describes the tool as reading a list of experiments with expected return format, but does not disclose idempotency, rate limits, or permissions. The conceptual explanation of experiments adds context but omits key behavioral details.

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 with two paragraphs and a code block. It front-loads the purpose and provides useful example usage and return format. Minor redundancy could be trimmed, but overall efficient.

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 provides a detailed example return object. However, it lacks parameter documentation, error handling, and edge cases. The tool has three parameters, one required in practice, but none are explained.

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%, yet the description does not explain any of the three parameters (limit, dataset_id, dataset_name). The example uses a dataset ID but does not clarify its role or the others. This is a critical omission for an agent to invoke the tool correctly.

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' with a specific verb and resource. It distinguishes from siblings by focusing on experiments per dataset and provides an example dataset ID usage.

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 gives an example usage suggesting when to invoke, but does not explicitly state when not to use it or compare to sibling 'get-dataset-experiments'. The context is implied but lacks explicit alternatives or exclusions.

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