compare_periods

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

No annotations are provided, so the description must bear full responsibility for behavioral disclosure. While it describes parameters and return structure, it does not mention whether the tool is read-only, any required permissions, data freshness constraints, or potential side effects. This leaves gaps for an agent to safely invoke the tool.

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 clear sections (usage scenarios, args, returns, examples). It is verbose but justified given the schema coverage gap. Minor redundancy exists between the Args list and the schema, but overall it remains readable and front-loaded with the core purpose.

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 parameters, return format, and provides examples. However, given the tool's complexity (6 parameters, 2 required) and no annotations, it lacks details on error handling, data availability (e.g., what if dates have no data?), and performance considerations. An output schema is mentioned (present), which helps, but the description could be more complete about edge 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 for parameters, but the description thoroughly explains every parameter: period formats (with examples like 'last_week' or date objects), compare_type options (with detailed effects), platform filtering, and top_n. This fully compensates for the schema's lack of documentation.

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 two time periods of news data across multiple dimensions (hot topics, platform activity, news count), using strong verbs like '比较' (compare) and '分析' (analyze). It distinguishes itself from sibling tools like 'aggregate_news' or 'analyze_topic_trend' by focusing specifically on period comparison.

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 includes a dedicated '使用场景' section with concrete use cases (week-over-week comparison, topic shift analysis, platform activity changes) and provides three explicit examples covering different parameter combinations, making it clear when to use this tool and how to configure it.

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