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 disclosure. It thoroughly explains parameter behaviors, including the critical requirement that date_range must be an object (not an integer), and gives format examples. It also clearly states that results are returned as JSON. It lacks details on potential side effects or error conditions, but for an analysis tool, this is reasonable coverage.

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 is appropriately sized for the tool's complexity. Every sentence contributes meaning—parameter descriptions are terse yet informative, and the three examples are illustrative without 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 presence of an output schema (so return values are covered) and the moderate complexity (5 parameters, 3 modes), the description is fully complete. It covers all parameter semantics, format constraints, defaults, and provides multiple usage examples, leaving no significant gaps for an agent to invoke the tool correctly.

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%, so the description must compensate, and it does so comprehensively. Every parameter is semantically explained: insight_type values with meanings, topic applicability, date_range format and example, min_frequency and top_n defaults with their mode context. This adds substantial value beyond the bare type information in the 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 with three specific modes (platform_compare, platform_activity, keyword_cooccur), each with a concise explanation. It explicitly names the resource (data insights) and the action (analyze), distinguishing it from siblings that are more specialized.

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 (e.g., platform_compare for cross-platform topic attention, keyword_cooccur for keyword pattern analysis), and includes practical examples. However, it does not explicitly exclude alternatives or compare against sibling tools like analyze_topic_trend, so it earns a 4 rather than a 5.

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