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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly enumerates what the tool provides: source, date computed, licence, and citation. It also implies a read-only metadata operation. While it does not explicitly state side-effect-free behavior or auth requirements, nothing suggests mutation or complexity beyond a lookup.

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?

The description is one tightly written sentence that front-loads the key content fields and closes with a practical usage instruction. Every word earns its place, and the title reinforces the purpose without bloating the description.

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, no-output-schema metadata lookup, the description is complete. It names the dataset, enumerates the returned provenance elements, and tells the agent when to use it. There are no hidden inputs or complex behaviors left unexplained.

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 and an empty input schema, so the baseline is 4. There is no parameter information needed, and the description correctly focuses on the tool's output rather than inputs. The description adds no param semantics because none exist.

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 clearly states the tool's purpose: it returns provenance metadata for the Sudslane dataset, specifically the source, computation date, licence, and citation. This clearly differentiates it from sibling tools like dataset_row or dataset_stats, which handle data access and analysis 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?

The description gives an explicit usage cue: 'Read this to attribute a figure correctly.' This tells an agent when to invoke the tool. It does not explicitly name alternatives or exclusions, but the use case is distinct enough from the data-oriented sibling tools that the guidance is sufficient.

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