많이 등장하는 주제어

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds that it operates on public data titles and returns keyword counts and weights, and explains the response_format difference, which provides useful behavioral context.

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?

Description is concise with a clear purpose sentence, a use-case sentence, and a structured Args/Returns block. Every sentence is informative without redundancy.

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?

For a simple read-only tool with full schema coverage and annotations, the description is adequately complete. It specifies the return structure and output formats, though it lacks mentions of failure modes or pagination, which are not critical here.

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 coverage is 100%, so parameters are documented. The description adds extra value by explaining the response_format values in plain language, clarifying that markdown is human-readable and json is structured.

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?

Description clearly states the tool returns top keywords from public data titles with occurrence counts. The use case '요즘 정부가 뭘 많이 하나' helps contextualize its utility compared to siblings like onegov_suggest_keywords.

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?

Description provides explicit context for use: browsing trending topics and selecting search candidates. However, it does not explicitly exclude scenarios or mention sibling tools as alternatives, leaving some differentiation implicit.

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