analyze_sentiment

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

No annotations are provided, so the description carries full responsibility. It discloses behavioral traits such as title de-duplication (limit param) and token-saving intent for include_url, but omits potential side effects, permissions, or rate limits. The coverage is partial but non-trivial.

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 with sections for Args, Returns, and Examples. It is concise yet informative, though slightly longer than minimal. Every sentence adds value, such as the suggestion to use resolve_date_range.

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?

Given six optional parameters, no annotations, but an existing output schema (not shown), the description provides enough context for an agent to invoke correctly. It includes an example call and clarifies return contents. Some edge cases like empty topic are not addressed, but overall completeness is good.

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?

Even though schema description coverage is 0%, the description thoroughly explains all six parameters with meanings, defaults, and formats (e.g., date_range format, platform examples). It adds significant value beyond the schema's bare types.

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 states a clear purpose: '分析新闻的情感倾向和热度趋势' (analyze sentiment and heat trends of news), with a specific verb and resource. It distinguishes this tool from sibling tools like search_news or get_news_by_date by its analysis focus, though it doesn't explicitly name alternatives.

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 a conditional recommendation: when using natural language dates, call resolve_date_range first. This is useful guidance for a specific scenario, but it does not explain when to choose this tool over siblings like analyze_topic_trend or aggregate_news, nor does it mention exclusions.

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