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

With no annotations provided, the description carries the burden, and it does well: it discloses that data is user-submitted aggregated data, specifies that it returns medians/ranges/sample size, and implies read-only behavior through '조회합니다'. It does not mention edge cases such as unknown institutions or sparse samples, but the sample-size disclosure partly mitigates this.

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?

Two dense, purposeful sentences: the first gives the operation and output, the second gives the usage boundary. No redundancy or filler.

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 two parameters, no annotations, and no output schema, the description covers the essential context: what the tool returns, how to invoke it, and when it is appropriate. It lacks guidance on empty results or data reliability beyond 'user-reported', but that is a minor gap.

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%, and the schema already documents institution and optional job_role with examples. The description does not add parameter-level detail beyond implying job-level breakdown, so the baseline of 3 is appropriate.

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 uses a specific verb ('조회합니다') with a concrete resource: successful-candidate spec data for Korean public institutions, and names specific returned metrics (median TOEIC, GPA, certifications, sample size). It also explicitly contrasts itself with competition-ratio lookup, distinguishing it from its sibling without ambiguity.

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?

It clearly states when to use the tool: when the user wants to know the spec level needed to pass rather than the competition ratio. It gives an implicit exclusion for the sibling tool, though it does not name lookup_competition_ratio explicitly.

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