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

Annotations already include destructiveHint=true and idempotentHint=true, and the description adds rich context beyond the annotations: it clarifies exactly what is destroyed (rows only, not schema/columns), what surface area is affected (own datasets only), and the API key requirement. No contradiction exists between description and annotations.

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?

Three short sentences, each earning its place: the first states the core action, the second gives the primary use case, and the third sets scope boundaries and authentication. Information is front-loaded and there is no 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?

For a single-parameter tool with rich annotations and no output schema, the description covers purpose, scope, destructive effect, what remains intact, and authentication. Nothing an agent needs to decide whether to call this tool is 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 100%, and the schema describes dataset_id as 'The UUID of the dataset to clear all rows from,' which is already complete. The description reinforces that this must be an owned dataset but adds no new syntax details beyond the schema.

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 first sentence names a specific verb ('Delete all rows'), a specific resource ('a dataset you own'), and a key qualifier ('keeping the schema and columns intact'). This clearly distinguishes the tool from destructive siblings like delete_dataset and from data-writing tools like write_rows.

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 provides clear context: use it to refresh your own uploaded table before re-importing. It also gives a strong exclusion rule—only own datasets are reachable, never public catalog or other users' data. It doesn't explicitly mention alternatives by name, but the usage scenario is sufficiently clear.

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