get_recommendations

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool is 'personalized' and an 'AI drama advisor', implying algorithmic curation, but does not disclose whether results are deterministic, how many results are returned, or what data is used for personalization. There is no mention of side effects, but as a recommendation tool, it is likely read-only. This is acceptable but lacks depth.

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 three sentences, front-loaded with the core function. Each sentence earns its place: the first defines the tool, the second provides practical examples, and the third clarifies language support. No fluff or 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?

The tool has 5 parameters, no required fields, and no output schema. The description and schema cover input parameters well, but the description does not explain what the response looks like (e.g., a list of items with IDs and titles), which is important for an agent to chain subsequent calls like get_content_detail. This is a notable gap given the absence of an output schema.

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%, so the schema already documents all parameters. The description adds a general note about supporting 'both Chinese and English genre/mood keywords' and references 'viewing context', which aligns with the 'mood' parameter. This adds slight value beyond the schema but doesn't introduce new parameter semantics that aren't already present.

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 tool's function: 'Get personalized content recommendations from WeTV/Tencent Video based on genre preferences, mood, or viewing context.' It uses a specific verb ('Get') and resource ('content recommendations'), and distinguishes itself from sibling tools like search_content and get_trending by emphasizing personalized, context-based suggestions. The 'AI drama advisor' phrasing reinforces its unique role.

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 concrete use-case examples like 'what should I watch tonight?' and 'recommend something for a date', making it clear when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools (e.g., get_trending for popularity-based picks, search_content for specific searches), leaving some implicit differentiation.

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