compare_periods

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 uses 'Returns:' section to describe the output structure clearly, and each compare_type is explained (overview, topic_shift, platform_activity). However, it doesn't disclose edge cases like behavior when no data exists for a period, or whether dates are inclusive/exclusive. Slight deduction for not mentioning potential failure modes or data availability requirements, but overall it adds meaningful context beyond the schema.

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 structured with clear sections (usage scenarios, Args, Returns, Examples) and uses markdown formatting. It's front-loaded with the purpose, then progressively details parameters and return values. While comprehensive, it's a bit long (~200 words in Chinese), but every section serves a purpose - no filler or redundant content.

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 the tool's moderate complexity (6 params, 3 compare types, output schema present), the description covers all bases: parameter formats, compare types with explanations, output structure, and working examples. The output schema exists so return values don't need exhaustive explanation, but the description still outlines the return fields. It's sufficient for an agent to use this tool effectively without additional clarification.

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?

With schema description coverage at 0%, the description completely compensates by explaining all 6 parameters in detail. For period1/period2 it specifies accepted formats (date objects or predefined strings like 'today', 'last_week'). It explains the topic parameter as optional keyword filtering, compare_type with three named values and their meanings, platforms as a filter list with examples, and top_n with default. Examples illustrate real usage of parameters in combination.

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 compares news data across two time periods, covering multiple dimensions like hot topics, platform activity, and news volume. It explicitly distinguishes itself from siblings like get_news_by_date (single date) and analyze_topic_trend (single topic trend) by focusing on two-period comparison. The verb '对比' (compare) and resource '时期' (periods) are specific and unambiguous.

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 dedicated '使用场景' (usage scenarios) section with concrete examples like comparing this week vs last week, and analyzing topic popularity differences across periods. It also includes multiple working examples showing typical call patterns. While it doesn't explicitly state when NOT to use it, the scenarios and examples clearly define appropriate usage contexts.

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