대학정보 API 실행

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

With no annotations, the description must disclose behavioral traits but only mentions execution and the Swagger-based param naming. It does not mention side effects, safety, error behavior, output format, or authentication requirements, leaving a significant transparency gap.

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 concise, front-loaded sentences with no wasted words. The essential information about purpose and parameter naming is delivered efficiently.

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 4 parameters, nested objects, and no output schema, the description is too sparse. It omits how to discover registered operations, what the response looks like, error handling, and safety implications. The Swagger note helps but leaves many operational gaps.

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 low (25%), but the description adds a crucial semantic: 'params' keys must match the Swagger spec exactly. It does not explain datasetId or operation, though these are somewhat self-evident from names. Partial compensation for low schema coverage.

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 tool executes registered university information API operations, with a specific verb ('실행합니다') and resource. It distinguishes from siblings like describe_operation and discover_datasets by focusing on execution.

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 context is implied: the tool executes operations, suggesting it is for running rather than describing or discovering. However, it does not explicitly state when to use it versus alternatives like describe_operation to obtain Swagger params first, nor mention any exclusions.

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