Datasets Get Data Model

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered by structured data. The description adds nothing beyond that baseline — no mention of what is returned, whether the data model must exist, or any permissions required.

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?

A single short, front-loaded sentence with no waste. It is concise, but the brevity reflects under-specification rather than well-edited density.

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 so return values need not be described, but the definition still omits the parameter's meaning and any differentiation from the many sibling datasets_* tools. For a lookup tool in a dense family, this is not enough for reliable selection.

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 data_model_id, and the description does not compensate at all — it never states what identifier format is expected (UUID, name, or path) or where to obtain it. For a one-required-param tool this leaves the only input undocumented in both places.

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 verb+resource ("Get data model metadata"), but "metadata" is vague and does not distinguish this tool from the sibling datasets_get_data_model_spec, which an agent could easily confuse it with. Without opening schemas, the agent cannot tell what class of metadata (spec, elements, columns) is returned.

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 no when-to-use guidance, no prerequisites, and never names an alternative such as datasets_get_data_model_spec or datasets_list_data_models. The agent must infer from the name alone when this is the right call.

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