list_datasets

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

With no annotations, the description carries the full burden and does well: it discloses the newest-first ordering, that test cases are omitted, that the API has no paging so an exactly-limit result may be truncated, the hard cap of 200, and that output is auto-stored as an object reference. It omits any notion of permissions or failure modes beyond 'an error message'.

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 core behavior is front-loaded, but the text bloats with Sphinx-style ':param'/':returns' lines that duplicate the prose, plus generic object-store boilerplate repeated across tools. Several sentences could be merged without losing meaning.

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?

For a two-parameter read tool with no output schema, the description covers return shape (dataset_id, test_case_count, pipeline_ids), pagination caveat, and object-store handling. Only permission/error specifics and the limit default are missing.

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 coverage is 0%, so the description must compensate, and it does: pipeline_name is defined as a filter on Datasets having a test case on that pipeline, and limit is bounded at 200. The default of 50 from the schema is not restated, leaving a small 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 opening sentence gives a specific verb, resource, ordering ('newest first') and scope exclusion ('without their test cases'), and the following sentences define what a Dataset is and what fields each entry carries. An agent can distinguish it from get_dataset and list_experiments without opening either schema.

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?

It explicitly routes the agent: read test cases with get_dataset, pass dataset_id to create_experiment. That is concrete when-to-use guidance for follow-up actions, though it never states when this tool is the wrong choice (e.g., searching vs listing).

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