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

No annotations are provided, so the description carries the disclosure burden. It discloses the returned fields (source, date, licence, citation), which is the core behavior, but it does not explicitly state that the operation is read-only or describe the return format. For a zero-parameter lookup this is adequate, though not rich.

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?

Two sentences with zero filler: the first enumerates the payload, the second gives the usage directive. The key content is front-loaded before the purpose statement.

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 zero-parameter tool with no output schema and no annotations, the description covers the payload and the purpose. It doesn't specify the response format, which is a minor gap given how simple and single-purpose the tool is.

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?

The tool has zero parameters, so there is nothing for the schema or description to document; the baseline of 4 applies. The description's enumeration of returned fields also reassures the agent that no configuration or input is required.

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 precisely what the tool delivers — source, compute date, licence, and citation for the PunchListWorks dataset — and attaches a concrete use case, attributing a figure. The provenance content (licence/citation) clearly sets it apart from the data-content siblings like dataset_columns, dataset_row, and dataset_stats.

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?

"Read this to attribute a figure correctly" gives an explicit trigger condition for calling the tool. It doesn't name alternatives or state when not to use it, but the sibling set, all about data values rather than metadata, makes the boundary obvious.

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