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

Annotations already mark it read-only/open/idempotent, and the description adds meaningful operational context beyond that: the beta CrossRef Data Citations endpoint, no authentication requirement, CC0 metadata, and a 5-day processing delay. This is exactly the kind of behavioral disclosure that helps an agent set expectations.

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 action and return fields are front-loaded in the first sentence, followed by tightly relevant use cases and API context. Every sentence earns its place; there is no redundant filler.

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 the annotations, complete parameter schema, and output schema, nothing essential is missing for an agent to call this correctly. The description adds the necessary data source, auth, freshness caveats, and purpose, making the tool self-sufficient.

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 input schema documents both parameters fully, including dataset_doi format and rows constraints, so the description carries little additional parameter burden. It mentions the dataset DOI conceptually but adds no value beyond the schema, matching the baseline for 100% schema coverage.

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 a specific verb and resource: it finds scholarly works citing a given dataset DOI and lists the exact returned citation fields. It does not explicitly call out sibling tools like citations.from_article or citations.browse, so it stops short of the highest tier.

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?

It gives clear context for when to use this tool, such as measuring dataset reuse/impact or tracing papers that relied on a dataset. However, it does not explicitly say when not to use it or point to alternative tools for related citation queries.

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