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 explains the return structure (JSON with periods, compare_type, and specific result keys) and the behavior of each compare_type, as well as defaults and optional filters. It does not explicitly state that the operation is read-only or describe error handling for invalid periods, but the read-only nature is implied by the analytical purpose and the detailed output spec.

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 (purpose, usage, arguments, return, examples) and is front-loaded with the primary purpose. Each sentence contributes value, and the examples illustrate realistic calls. Despite its length, it is efficient and avoids redundancy, making it easy for an agent to parse.

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?

For a tool with 6 parameters and no annotations, the description is comprehensive. It covers all parameters, explains the output schema, provides multiple examples, and clarifies the semantics of each compare_type. The output schema exists, reducing the need to detail return fields, but the description still summarizes the result keys. No essential information for correct invocation is missing.

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?

Schema description coverage is 0%, making the description the sole source of parameter meaning. It thoroughly explains all six parameters: period1 and period2 with accepted formats (object or preset strings), topic, compare_type with enumerated values and descriptions, platforms as a filter list, and top_n with default. This goes well beyond the schema's type-only definitions and adds critical usage context.

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 purpose: '时期对比分析 - 比较两个时间段的新闻数据' and enumerates the dimensions compared (hot topics, platform activity, news count). It is specific about the resource (news data) and the action (compare two periods), distinguishing it from trend-analysis siblings like analyze_topic_trend, which focus on a single series rather than pairwise 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 '使用场景' section with three concrete examples (comparing this week vs last week, topic heat differences, platform activity changes), giving clear context for when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools, though the sibling list includes related analytics tools. This is a minor gap.

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