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

With no annotations, the description carries the full burden and does supply real behavioral context: results are scoped to the user's personal library, the call costs '[Free · 1 credit]', and paper_id must be a HypePaper papers.id rather than a kg_nodes id. It does not say what is returned (papers only, or papers with a relation/evidence) or whether there are result limits.

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?

Front-loaded with the action and scope, then trigger examples, then the ID caveat and cost. Every clause is useful; only '[Free · 1 credit]' is slightly ambiguous in signaling free versus costing a credit.

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 one-parameter, annotation-free lookup tool with no output schema, the definition covers scope, trigger, ID domain, and cost. The remaining gap is the shape of the result, which an agent could infer from the verb but must discover at call time.

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?

Schema coverage is 0% and the single property is an untyped bare string, so the description must compensate. It does so meaningfully by naming the exact ID namespace (HypePaper papers.id, not kg_nodes id), which is the one realistic source of invocation error here.

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?

States a specific verb and resource: finding papers that extend or improve on a given paper's methods/algorithms. That is a sharper semantic relation than a generic 'related papers' tool, so an agent can separate it from siblings like similar_papers or library_find_contradictions, though no sibling is named explicitly.

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?

Gives concrete trigger phrasing ('what's been done since paper X?', 'who extended this method?'), which maps cleanly onto user intent. It lacks any when-not-to-use guidance or a named alternative for the 'just similar work' case.

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