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

Annotations only cover safety (readOnlyHint, openWorldHint=false); the description goes well beyond them by disclosing the cost model (free), the sampling cap (up to 10 rows, never more than half the dataset), the exact price field returned, and the JSON output format. An agent knows exactly what calling it costs and yields.

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 tight sentences with no waste, front-loaded with the cost signal ('FREE') and the routing instruction before the return-value detail. Every clause earns its place.

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?

With no output schema, the description fully compensates by enumerating the return payload and its bounds. For a one-parameter read-only preview tool, nothing needed to call it correctly 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?

There is a single parameter with 100% schema description coverage ('Id from list_datasets'), so the schema already carries the semantics. The description adds no extra format or sourcing detail beyond that, which is the baseline case for high coverage.

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 (preview) and resource (dataset) and enumerates exactly what is returned: up to 10 rows, field names, total row count, created_at, and the exact USDC price. This clearly distinguishes it from list_datasets (enumeration) and buy_dataset (purchase).

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?

Explicitly says 'Use before buy_dataset to check a dataset fits your task', naming the sibling alternative and the condition that selects this tool. The 'FREE' lead-in further signals it is the zero-cost pre-purchase step.

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