analyze_sentiment

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 details several behaviors: default date range is today, limit defaults to 50 with a maximum of 100, titles are deduplicated, sort_by_weight defaults to true, include_url defaults to false to save tokens, and the return format is JSON with sentiment distribution, heat trends, and related news. This goes beyond a simple operation description, though it lacks explicit read-only or error behavior details.

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: a one-sentence purpose, a useful usage tip, a clear Args list, Returns overview, and an Example. Each section is concise and earns its place, with no unnecessary repetition or verbose explanations.

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 inputs with defaults, provides a relevant usage tip, and summarizes the output structure. It does not address error scenarios or what happens with empty results, but given the presence of an output schema and the tool's moderate complexity, the description is sufficiently complete for correct invocation in typical use cases.

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, but the tool description provides thorough explanations for all six parameters in the Args section. It specifies formats (e.g., date_range as {'start': 'YYYY-MM-DD', 'end': 'YYYY-MM-DD'}), defaults, and semantic notes (e.g., deduplication, token saving). This fully compensates for the schema's silence and adds significant meaning beyond the raw property 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 clearly states the tool's purpose: '分析新闻的情感倾向和热度趋势' (analyze sentiment and popularity trends of news). It uses a specific verb+resource structure. However, it does not explicitly distinguish itself from sibling tools like analyze_topic_trend or analyze_data_insights, which could overlap in scope.

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 an explicit usage guideline: when using natural language dates, first call resolve_date_range to obtain a precise date range. This gives clear contextual guidance for a common scenario. It does not explicitly state when to use this tool over sibling analysis tools, but the parameter descriptions and defaults also aid usage.

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