Datasets List Data Model Columns

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

Annotations already establish that this is a safe read-only operation, and the description adds meaningful behavior beyond that: it follows pagination to the last page and explains how entries, total, nextPage, and hasMore are handled. It stops short of covering authorization or error behavior, but the pagination detail is genuinely useful.

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 front-loaded with the core operation and then explains pagination behavior without excessive repetition. The second sentence is long and somewhat technical, but it contains useful information rather than filler.

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 is simple and has an output schema, so return values and the read-only safety profile are adequately covered elsewhere. However, the only input parameter is undocumented, which is a notable completeness gap for an agent trying to call the tool correctly.

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?

There is one required parameter, data_model_id, and the schema provides 0% description coverage, so the description must carry the burden. It never explains what data_model_id is, what format it uses, or where to obtain it; the phrase 'in a data model' only implies the parameter exists.

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 states a specific verb and resource: it lists all columns across all elements in a data model. That scope is clear enough to distinguish it from table-level column listing, though it does not explicitly name a sibling alternative.

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 intended use is implied by the phrase 'across all elements in a data model,' but there is no explicit when-to-use guidance, no exclusions, and no routing to alternatives such as listing columns for a table or getting columns for a single element.

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