Google Scholar Author Alternative: Find Experts & KOLs by Topic

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

Goes well beyond the annotations by disclosing the pricing model ($0.0025 per expert), the no-charge-for-empty-runs policy, that the call runs on the user's own Apify account, and that spend is hard-capped via run_max_charge_usd. These are exactly the operational facts an agent needs before triggering a paid, open-world call.

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-loads purpose, then input, then output, then operational/cost details — a readable order with no filler. The second paragraph is dense but each sentence (actor, price, billing exception, cap) carries distinct information.

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 an 18-parameter paid tool with no output schema, the description supplies the missing return-value shape (ranked experts with ORCID, affiliation, h-index, topic papers) plus cost and timeout behavior. Minor gap: it doesn't mention the ranked-pool size limit (200 authors) or latency expectations in prose.

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 100%, so every parameter is already well documented; the description only summarizes the input as "a topic plus country, institution and year filters." That summary adds almost nothing beyond the schema, so the baseline 3 applies.

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 academic experts and KOLs on a research topic — and positions itself against an external reference ("Google Scholar author search alternative"), which makes it clearly distinct from siblings like literature_search or pubmed_evidence_table.

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 opening "Use this to find..." gives a clear usage context and the Google Scholar framing implies when an agent would pick it over a paper-search tool. It does not, however, explicitly name a sibling tool or state when-not to use it, so routing still requires inference.

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