analyze_data_insights

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 mentions the return format ('JSON format data insights analysis results') and provides parameter-specific constraints (e.g., date_range must be object format, not integer). However, it doesn't cover important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens with invalid parameters.

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 (Args, Returns, Examples) and uses bullet points effectively. While somewhat lengthy due to detailed parameter documentation, every sentence earns its place by adding necessary information. The front-loaded purpose statement is clear, though the Chinese title adds minor redundancy.

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, 3 distinct analysis modes) and 0% schema description coverage, the description does an excellent job of explaining parameter usage and relationships. The presence of an output schema means the description doesn't need to detail return values. However, without annotations, it could better address behavioral aspects like error conditions or performance characteristics.

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 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. It explains each parameter's purpose, lists valid values for insight_type with descriptions, specifies format requirements for date_range with examples, indicates which parameters apply to which modes, and provides default values. This adds substantial meaning beyond the bare schema.

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 this is a 'unified data insights analysis tool' that 'integrates multiple data analysis modes' and lists three specific insight types. It provides a clear verb ('analyze') and resource ('data insights'), though it doesn't explicitly differentiate from sibling tools like analyze_sentiment or analyze_topic_trend.

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 implies usage through parameter documentation (e.g., 'topic' is applicable to platform_compare mode, min_frequency and top_n are for keyword_cooccur mode). However, it doesn't explicitly state when to choose this tool over alternatives like analyze_topic_trend or compare_periods, nor does it provide exclusion criteria or prerequisites.

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