get_recommended_statistics

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

With no annotations, the description bears full responsibility for disclosing behavioral traits. It states 'recommends', indicating a read operation, but omits details about authentication, rate limits, or any potential side effects. There is no mention of what the recommendation algorithm considers or how results are ordered.

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 sentence with no wasted words. It is front-loaded with the key action and resource, making it easy to parse. Every word contributes to the purpose.

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?

The description covers the basic purpose but lacks information about return format, pagination, or how recommendations are generated. Given no output schema, the description could be more helpful by indicating what kind of data (e.g., table IDs, names) will be in the response. However, for a simple recommendation tool, it is minimally adequate.

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% (both parameters have descriptions in the schema). The tool description does not add any extra meaning beyond the schema, which already explains the 'topic' enum and 'limit' default/range. 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 clearly states the verb '추천합니다' (recommends) and resource '주요 통계표' (key statistical tables) with the context of '관심 분야에 맞는' (according to area of interest). It implies filtering by topic but does not explicitly differentiate from sibling tools like 'search_statistics' or 'get_statistics_list', which could also provide topic-based results.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies usage when you need recommendations based on a selected topic, but it does not mention exclusions or compare with siblings such as 'quick_stats' or 'compare_statistics'.

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