compare_universities

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'source-aware research' and 'U.S. federal metrics' but does not explain what these entail, how data is aggregated, whether it requires network calls, rate limits, or any side effects. The phrase 'source-aware' is vague and does not disclose potential deviations or limitations. The description does not contradict any structured data (no annotations exist), but it fails to provide meaningful behavioral transparency.

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 very short and front-loaded, stating the core purpose and the input requirement in two sentences. No filler or redundant content. It is appropriately sized for a tool with a single parameter, though it sacrifices some detail for brevity. Structure is clean with an 'Args' section that mirrors the schema.

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?

Given the complexity of comparing multiple universities, the description is incomplete. While an output schema exists (not shown), the description does not explain what aspects of universities are compared (e.g., rankings, funding, research output) or what 'source-aware research' and 'federal metrics' mean. It also lacks error-handling or null-return guidance. The description is too terse to fully prepare an agent for invoking the tool correctly in diverse contexts.

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 schema only defines institution_ids as an array of strings with 0% description coverage. The description compensates by specifying that IDs are OpenAlex institution IDs and the expected count (two to five). This adds meaning beyond the type definition and clarifies the format and cardinality, which is valuable for correct invocation. It doesn't cover all nuances (e.g., exact ID format), but it provides the key semantic information.

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 the action ('Compare') and the resource ('universities'), with a specific range of two to five. It also hints at the scope ('source-aware research and U.S. federal metrics'), which differentiates it from siblings like search_universities (search) and get_university_profile (single profile). It doesn't explicitly name a sibling, but the verb and range make the purpose distinct.

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 implies usage by the action 'compare' and the range of IDs, but it does not explicitly state when to use this tool versus alternatives like get_university_profile or search_universities. No conditions, exclusions, or alternative recommendations are provided, so an agent must infer the correct context. The description is adequate but not explicit about when-not-to-use.

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