analyze_data_insights

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

With no annotations, the description carries full burden for behavioral disclosure. It indicates the tool returns JSON results and analyzes data, implying read-only behavior, but does not explicitly state it has no side effects or whether it requires specific permissions. This is adequate but not thorough.

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 sections (Args, Returns, Examples) and uses bullet points for clarity. While slightly verbose, every sentence adds value. No filler.

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 output schema exists, the description does not need to detail return values. It covers all 5 parameters, explains each insight_type, and provides multiple usage examples. For a multi-mode analysis tool, this is comprehensive.

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 no parameter descriptions, so the description fully compensates by explaining each parameter: insight_type with enumerated options, topic applicability, date_range format with a critical object-type note, and defaults for min_frequency and top_n. Examples further clarify usage.

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's a unified data insight analysis tool with three specified modes (platform_compare, platform_activity, keyword_cooccur) and briefly describes each. However, it does not explicitly differentiate from sibling tools like analyze_sentiment or analyze_topic_trend, leaving potential overlap ambiguous.

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 clear context for when to use each insight_type through its description and examples. However, it lacks explicit guidance on when not to use the tool or alternatives among siblings, which would strengthen it.

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