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

No annotations are provided, so the description carries the burden. It reveals the returned metadata fields, which is useful behavioral context, but is silent on permissions, whether the dataset is fixed, or any freshness/rate considerations. For a zero-parameter read-only metadata tool the risk is low, but disclosure is thin.

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 tight sentences, front-loaded with the payload contents and closing with the actionable reason to call it. Every clause earns its place with no redundancy.

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 usefully enumerates the fields an agent will receive (source, computed date, licence, citation), which compensates for the missing return-value documentation. It is nearly complete for a trivial zero-arg tool, lacking only freshness or format details.

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 takes zero parameters, so per the rubric the baseline is 4. Nothing in the description is needed to explain arguments, and it correctly introduces no parameter concepts.

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 names the specific content the tool returns (source, computed date, licence, citation) and scopes it to the Trading Card Value Checker dataset, so an agent can distinguish this metadata lookup from the data-oriented siblings like dataset_row and dataset_stats. It is phrased as a noun list rather than a verb+resource, but the purpose is unambiguous.

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 closing sentence 'Read this to attribute a figure correctly' gives one clear use case. However, it names no alternative and offers no explicit when-not guidance, and none of the sibling tools are referenced, so the routing signal is only implied.

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