analyze_sentiment

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

Since no annotations are provided, the description carries the burden of behavioral disclosure. It supplies meaningful behavioral details: limit=100 deduplicates titles, sort_by_weight defaults to True, include_url defaults to False to save tokens, and date_range defaults to today. It does not explicitly state whether the operation is read-only or whether rate limits apply, but the analysis-oriented behavior is still well described.

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 well structured: purpose, routing advice, args, return shape, and an example. There is some length, but the parameter details and examples justify it, and there is little wasted text.

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 all parameters, a calling pattern, an example, and the general return shape. Because an output schema is present, the lack of detailed return-field documentation is acceptable. The main remaining gaps are the absence of a full list of valid platform IDs and an unclear handling of the string-type date_range variant.

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?

The input schema has 0% description coverage, so the description fully compensates. All six parameters are explained with intended meaning, default values, formats, and practical examples. For instance, date_range is given an explicit JSON structure and platforms is shown with concrete example values.

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 explicitly says 分析新闻的情感倾向和热度趋势, giving a specific verb and resource. It is clear about what the tool does, but it does not directly differentiate itself from close sibling tools like analyze_topic_trend or analyze_data_insights.

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?

There is a useful routing hint to call resolve_date_range first for natural-language dates, and the parameter list provides defaults and examples. However, there is no explicit guidance about when this tool should be used instead of analyze_topic_trend or analyze_data_insights, so usage is mostly implied rather than stated.

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