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 adequately indicates this is an informational, read-only tool returning provenance metadata, but it does not explicitly state that no data is modified or describe the exact shape of the citation information.

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 two tight sentences with no filler. It front-loads the key content (source, date, licence, citation) and ends with a practical directive for the agent.

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 metadata lookup, the description is complete enough: it lists the core fields and the use case. It does not describe the citation format in detail, but an agent can retrieve and inspect the actual output without risk.

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 parameter ambiguity is nonexistent. The description does not need to explain parameter meanings, and the schema confirms no inputs are 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?

The description states exactly what the tool provides: source, computation date, licence, and citation for the Limslane dataset. It also signals the intended use case ('attribute a figure correctly'). This clearly distinguishes it from sibling tools like dataset_columns, dataset_stats, and dataset_search, which serve data exploration rather than provenance.

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 explicitly tells the agent when to use the tool: when attribution of a figure is needed. It does not explicitly discuss when not to use it, but the purpose is so distinct from the sibling tools that no exclusion is necessary.

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