trend

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

With no annotations provided, the description carries the full burden, and it does disclose key behavioral traits: the 12-month window, monthly granularity, and the normalized 0-100 scale. This meaningfully tempers the agent's expectations about what the returned values represent, though it omits edge-case behavior such as missing data or query limits.

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 sentences, no filler, and the key facts are front-loaded: source, time range, and normalized return values. Every sentence contributes meaningful information.

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 single-parameter tool with no output schema, the description is mostly complete: it identifies the source, input, time span, and high-level return semantics. It does not specify the exact response structure, but for this simple tool that is not a critical 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 coverage is 100%, with the single 'keyword' parameter already described as 'Keyword to search'. The description adds no additional parameter semantics beyond what the schema provides, so the baseline score 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 ('Get') with a precise resource ('12-month search trend from Naver DataLab') and defines the output as monthly relative values from 0 to 100. This clearly differentiates it from volume-focused siblings like search_volume, since it describes interest change over time rather than raw counts.

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 says what the tool does but gives no guidance on when to choose it over alternatives such as search_volume, keyword_expand, or batch_analyze. There is no explicit context, prerequisite, or exclusion to help an agent decide between siblings.

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