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

With no annotations supplied, the description carries the full behavioral burden, and it does disclose substantive traits: the hybrid keyword+embedding ranking, the semantic-nearest fallback when no keyword match exists, the breadth of entity types returned, and the '[Free · 1 credit]' cost signal. It omits pagination behavior, whether costs apply per page vs per call, and any auth or rate-limit constraints.

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 scope statement is front-loaded and the ranking detail follows it, with a compact '[Free · 1 credit]' tag at the end. The single dense sentence packs a lot in and wastes nothing, though the parenthetical ranking clause is somewhat syntactically heavy.

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 six-parameter, no-annotation, no-output-schema tool, the description covers breadth of results and ranking behavior well but leaves the parameters and the shape of the combined multi-entity response undescribed. An agent can decide to call it, but cannot confidently predict how to paginate or how the mixed entity results are structured.

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 description coverage is 0% across six parameters, so the description must compensate and it does not: q, sort (relevance/hype/recent), limit, offset, year_min and year_max receive no explanation. The mention of 'keyword matches' and 'nearest by meaning' hints that ranking relates to the sort parameter but never clarifies what 'hype' means or how year filters interact with relevance ordering.

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 (search) and scopes the resource precisely: 'the whole HypePaper corpus' across papers, topics, authors, datasets, affiliations, and a topic expert. It even explains how paper results are produced (keyword matches ranked by keyword+embedding fusion, with nearest-by-meaning fallback), which tells an agent exactly what kind of result set to expect and distinguishes it from narrower lookups like author_papers or get_paper.

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?

'Start here.' is an explicit entry-point recommendation, which is real usage guidance for an agent deciding where to begin. However, no alternatives are named (e.g., when to prefer expert_ask or library_search_nodes over this) and no exclusion conditions are given, so it stops short of full when/when-not routing.

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