Get University Metrics

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

The description adds context about data quality ('verified'), scope ('local'), and conditional availability ('when available'), which complements the readOnlyHint annotation. It does not describe return format or error behavior, but the annotation already covers the safety profile, so the added context earns a mid-range score.

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 a single, succinct sentence with no filler words. It is front-loaded with the core purpose and every word contributes to the meaning, making it highly concise and well-structured.

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?

The description is minimal and omits critical information about the 'indicators' parameter and what 'metrics' entails. Given the absence of an output schema and 0% schema coverage, this leaves the agent without enough context for reliable invocation, especially since the tool has two parameters.

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?

With 0% schema description coverage, the description should compensate for unclear parameters. It hints that university_name is the subject ('one university') but provides no explanation for the 'indicators' array or its role in filtering metrics. This is a significant gap in parameter understanding.

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 (return) and the resource (verified local metrics for one university), making the tool's purpose distinct from siblings like compare_universities or search_university. However, it doesn't explicitly name alternatives or differentiate itself from list_indicators, so it falls short of a perfect score.

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 for retrieving metrics of a single university ('for one university'), but provides no explicit guidance on when to choose this tool over siblings such as search_university or compare_universities. No exclusions or alternative recommendations are given, making the usage context only implicit.

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