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

No annotations are provided, so the description carries the full burden. It does disclose the return content (source, date, licence, citation), making the read-only informational nature self-evident. It omits any statement about freshness guarantees or where the metadata originates, which would be the only remaining gap.

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, front-loaded with the content list and closed with the reason to call it. No filler. Slightly under-budgeted rather than verbose.

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 and no parameters, the description must convey what comes back, and it lists the four key returned attributes. That is sufficient for an agent to call and consume it correctly, though a note on whether the metadata is static or versioned would fully close the loop.

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 baseline there is nothing for the description to disambiguate. Its mention of returnable fields is a bonus, not a requirement.

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 exact resource (the PotterySuppliesHQ dataset) and enumerates the specific fields it exposes: source, computation date, licence, and citation. This clearly separates it from sibling tools like dataset_stats, dataset_columns, and dataset_search, which cover data rather than attribution metadata.

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 a concrete when-to-use condition tied to citation/attribution. It stops short of naming alternatives or stating when NOT to call it, but the usage context is unambiguous for a zero-parameter info tool.

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