get_article_recommendations

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions the recommendation basis (user interests and content quality) but does not disclose potential side effects, authentication requirements, pagination behavior, or return format. The verb '获取' implies a read-only operation, but this is not explicit.

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 with no filler. It is front-loaded with the core action and resource, making it easy to scan.

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 tool has simple parameters and no output schema, so the description is moderately sufficient. However, the lack of usage guidance and behavioral details, combined with the abundance of sibling tools, makes the context incomplete. It describes what it does but not fully when or how it behaves.

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 each parameter described, but the description adds cohesive meaning by framing the parameters around 'personalized' and 'content quality'. This explains how user_interests and min_quality_score interact, providing value beyond the individual schema descriptions.

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 '获取个性化文章推荐，基于用户兴趣和内容质量' clearly identifies the action (get recommendations), the resource (articles), and the distinguishing criteria (personalized, based on interests and content quality). This effectively differentiates it from sibling tools like get_trending_articles or get_pin_recommendations.

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 usage guidance is provided. There are multiple recommendation-related siblings (e.g., get_trending_recommendations, get_user_recommendations, get_simple_recommendations) but the description does not state when to use this tool instead of alternatives or what exclusions apply.

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