analyze_data_insights

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that results are returned as JSON and describes analysis modes, but it never clarifies whether the tool is read-only, whether it triggers expensive computation, whether it depends on external data sources, or whether any state is changed. This leaves a meaningful transparency gap.

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 organized with an intro, Args, Returns, and Examples sections. Every line adds value: mode definitions, parameter constraints, and concrete usage examples are all present without redundant filler. It is long enough to be useful and compact enough to scan quickly.

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 three modes, conditional parameters, and subtle date_range typing, the description covers the essential invocation knowledge: which parameters apply to which mode, defaults, formats, and example calls. It does not explicitly explain what happens when irrelevant parameters are passed or describe error cases, but an output schema exists and the provided information is sufficient for basic correct usage.

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%, but the description provides rich semantics for all five parameters: allowed insight_type values with mode meanings, optional topic, strict date_range object format with example and a warning against integers, plus defaults for min_frequency and top_n. This is exactly what an agent needs to construct valid calls and goes far 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 identifies an analysis tool for multiple data insight modes, enumerating three specific insight types with plain-language explanations. It does not explicitly differentiate itself from overlapping sibling tools like analyze_topic_trend or analyze_sentiment, so it misses a bit of sibling-targeted clarity.

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 shows when to use each mode through the insight_type options and concrete examples, but it never states when not to use the tool or which alternative sibling covers a similar case. Usage guidance is implied rather than explicit, especially given the large sibling set with analysis-related tools.

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