scienceon_organization_details

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

The readOnlyHint annotation already informs the agent this is a read-only operation. The description adds no additional behavioral context (e.g., return format, limitations, side effects), so it meets the baseline without exceeding it.

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, concise sentence that immediately communicates the tool's purpose and primary parameter. It is front-loaded and contains no unnecessary words.

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 one well-documented parameter, an output schema present, and annotations indicating read-only behavior, the description is sufficient for an agent to select and invoke the tool correctly. The description covers the purpose, and the structured data covers the rest.

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 already documents the 'cn' parameter with a description of its meaning and origin from search results. The description merely states 'by CN number,' which adds no new information beyond the schema, so the score is at the baseline for high 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's function: retrieving detailed research institution information by CN number. It uses a specific verb ('조회합니다') and resource ('연구기관 상세정보'), and the 'CN번호로' qualifier distinguishes it from list-style sibling tools like scienceon_organizations.

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 the prerequisite of having a CN number to use this tool, providing clear context for when it is appropriate. However, it does not explicitly mention alternatives like scienceon_organizations for list queries, so it has no exclusions or direct sibling differentiation.

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