Datasets List Data Model Elements

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

Annotations already declare readOnlyHint=true/openWorldHint=true, so the safety profile is covered. The description does add real behavioral detail beyond that — it auto-follows pagination to the last page and reports nextPage as null/hasMore as false — but it says nothing about page size, rate limits, or what happens on partial failure mid-pagination.

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 purpose is front-loaded in the first sentence and the pagination detail follows without padding. The second sentence is dense with internal jargon ('Sigma's page', 'Sigma's value') but no sentence is redundant.

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?

An output schema exists and the description still clarifies the aggregated return shape, which is welcome. However, for a list tool nested among many similar dataset tools, the missing selection guidance and absent parameter explanation leave gaps an agent would need to close by guesswork.

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% for the single required parameter, so the description must compensate and does not: data_model_id is never mentioned, nor is its format or where to obtain it. The parameter name is largely self-explanatory, which keeps this above a 1, but nothing is added.

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?

States a specific verb and resource ('List elements in a data model'), which an agent can read at a glance. It does not distinguish itself from near-siblings like datasets_list_data_model_columns or datasets_list_data_model_sources, so the boundary between 'elements' and 'columns'/'sources' stays implicit.

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 contains no when-to-use guidance, no prerequisites, and never names an alternative. With several data-model listing tools present, the agent gets no help choosing between them.

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