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

Goes well beyond the readOnly/openWorld annotations: discloses the pay-per-call model ($0.002 USDC), the payment rails (x402 on Base, Solana, Arc), and that only successful calls are charged. That billing behavior is exactly the kind of risk context an agent needs before invoking.

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 the dataset identity and scope before the pricing note; every clause carries information. It is dense but one long sentence, which slightly taxes scanning, keeping it off a 5.

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?

With 8 fully documented params, zero required, an output schema, and price/auth details included, an agent has enough to invoke correctly. Sorting/ordering of results and pagination semantics are the only remaining gaps, and the output schema likely covers them.

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 100% and every parameter already documents defaults and enums inline. The description only loosely restates the field/dimension/period surface, adding no syntax or constraint detail the schema does not already carry, so 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?

Names the exact source (CWTS Leiden Ranking Open Edition 2025, publication-based, OpenAlex, ~2,800 universities) and the exact output (rank, name, country, dimension indicators). An agent can distinguish this from search_shanghai_arwu_rankings or search_university_rankings_mega_pack purely from the description.

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?

Usage is implied by the resource itself and by the 'for a country or continent, field and period' framing, but there is no explicit when-to-use/when-not or routing to sibling rankings tools (ARWU, GRAS, mega pack). The agent must infer selection from data-source names alone.

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