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 implies a read operation ('Read this') and lists the returned fields, but it does not explicitly state that the tool is read-only, has no side effects, or requires no special permissions. For a no-parameter metadata lookup this is adequate but not fully transparent.

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 no wasted words, front-loading the returned information and following with a clear usage instruction. The structure is efficient and easy to parse.

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?

Given no output schema and no parameters, the description lists the exact fields returned (source, date, licence, citation), which is sufficient for an agent to understand the result. It could mention that the tool is read-only or that the dataset is fixed, but overall it is complete for this simple tool.

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 the schema cannot add meaning. The description appropriately does not discuss parameters, and the baseline for 0 params is 4.

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 states exactly what provenance information is returned (source, computed date, licence, citation) for the named dataset, which distinguishes it from data-querying siblings like dataset_columns or dataset_stats. An agent can immediately tell this tool is for attribution, not data retrieval.

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 sentence 'Read this to attribute a figure correctly' gives a clear when-to-use condition. However, it does not name any alternative tool or explain when not to use this one, leaving some routing inference to the agent.

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