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

With no annotations provided, the description fully reveals behavior: it performs a forward citation search using PubMed Central data, returns a list of citing articles, and is read-only. No contradictions or hidden traits.

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 well-structured with sections, emojis, and examples, making it scannable and informative. It could be slightly more concise, but every section serves a purpose.

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?

The description covers all necessary context: it explains the direction of citation search, use cases, parameter details, and provides an example. With an output schema present for return values, no further details are needed.

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 description adds significant value beyond the input schema: it specifies acceptable pmid formats (e.g., '12345678', 'PMID:12345678') and explains the limit range (1-100) and default (10). This compensates for the 0% schema description 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 clearly states it finds articles that cite a given PubMed article, using specific verbs and resource. It distinguishes from sibling tools like get_article_references (backward search) and find_related_articles (topic-based), ensuring the agent selects the correct tool.

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 lists use cases (e.g., track research impact, find follow-up studies) and complementary tools, providing clear guidance on when to use this tool versus alternatives. An example further clarifies usage.

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