quick_trend

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

No annotations provided, so the description carries full burden. It discloses the return format (10 years data, change rate, highest/lowest points, trend summary) and keyword extraction rules. However, it lacks details on data source, rate limits, or any side effects.

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 uses bullet points and clear sections (usage time, return format, keyword tips). It is front-loaded with purpose. However, it is slightly verbose with Korean examples and could be more concise.

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 3 parameters, 1 required, no output schema, and no annotations, the description covers purpose, usage guidance, return format, and parameter semantics. It does not explain return values in detail, but the summary is sufficient for a simple trend analysis tool.

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%, baseline 3. The description adds value by providing examples of what to put in 'keyword' (e.g., '인구', '출산율') and explaining that trend-related words should be removed. It also clarifies the 'region' parameter's optionality.

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 states the tool analyzes time series trends for questions about trends, changes, increases, decreases (e.g., "~추세", "~변화"). It uses specific verbs and resources, but does not explicitly differentiate from sibling tools like analyze_time_series.

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 provides explicit guidance on when to use the tool: when the question involves trend/changes in Korean keywords. It gives examples of keyword extraction, but does not mention when not to use it or point to alternatives.

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