analyze_data_insights

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

No annotations are provided, so the description must fully disclose behavioral traits. It specifies that returns are in JSON format and includes important parameter constraints (e.g., date_range must be an object, not an integer). However, it does not address side effects, permission requirements, rate limits, or error behavior. The description is adequate but not comprehensive, leading to a score of 3.

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 (general description, Args, Returns, Examples). Every sentence is informative and contributes to understanding. It is concise (about 150 words in Chinese) without redundancy. The use of bullet points and formatting (bold, line breaks) enhances readability. This is an excellent example of conciseness and structure.

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 complexity (5 parameters, multiple modes) and the presence of an output schema (though not fully visible), the description covers all essential aspects: parameter definitions with defaults, examples, and a brief return type. It does not explain error handling or behavior with invalid inputs, but this is acceptable for a read-only analysis tool. The completeness is high, lacking only minor edge-case details, hence a score of 4.

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, so the description carries the full burden. It thoroughly explains each parameter: insight_type with enumerated options and mode descriptions, topic as optional, date_range with required format and important note, min_frequency and top_n with defaults and applicable modes. Examples further clarify parameter usage. This exceeds basic requirements and adds significant meaning, warranting a score of 5.

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 it is a unified data insight analysis tool integrating multiple analysis modes (platform_compare, platform_activity, keyword_cooccur). The purpose is specific and actionable, but it does not explicitly differentiate from sibling analysis tools such as 'analyze_sentiment' or 'compare_periods', which have overlapping functions. The lack of sibling differentiation lowers the score from 5 to 4.

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 examples for each insight_type, implying when to use each mode (e.g., for platform comparison, platform activity, or keyword co-occurrence). However, it does not state when NOT to use this tool or mention alternative sibling tools. For instance, it does not clarify when to use this tool versus 'analyze_sentiment' for sentiment analysis. The guidance is implied but not explicit, earning a score of 3.

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