analyze_sentiment

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

With no annotations, the description discloses key behaviors: deduplication of titles, default parameter values, and token-saving via 'include_url'. It does not mention authentication, rate limits, or error conditions. The output format is briefly described but not detailed.

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, guidance, parameter list, return type, and an example. Every sentence adds value, and it is front-loaded with the core purpose. The length is appropriate for the complexity.

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 6 parameters, no annotations, and an output schema that exists, the description covers all user-facing aspects: parameter semantics, return structure, and a usage suggestion. It lacks details on error handling or edge cases, but it is mostly complete for a tool with 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?

The schema has 0% description coverage, but the description compensates by explaining each parameter's meaning, format, and defaults. It provides examples for 'platforms' and 'date_range', though it omits the 'string' variant for 'date_range' allowed by the schema. Overall, it adds significant value.

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 analyzes sentiment and heat trends of news. It uses a specific verb ('分析') and resource ('新闻的情感倾向和热度趋势'), which distinguishes it from siblings like 'search_news' or 'get_latest_news' that lack sentiment analysis focus.

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 recommendation to use 'resolve_date_range' for natural language dates, which is useful. However, it does not explicitly state when to prefer this tool over alternatives like 'analyze_topic_trend' or 'find_related_news', nor does it mention contraindications.

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