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 behavioral burden. It only states that results are returned as JSON; it does not disclose whether the operation is read-only, what data source it reads from, whether there are rate limits, or any side effects. For an analysis tool this is a meaningful 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 Args, Returns, and Examples sections, with the unifying purpose front-loaded. Each parameter entry earns its place, and the examples demonstrate realistic calls across all three modes. Redundancy with schema defaults is minor.

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 (3 modes, 5 params, no annotations), the description is nearly complete: it covers every parameter, mode, format nuance, and return type, and the output schema covers return shape. It lacks only broader operational context such as data source, prerequisites, or limitations.

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 fully compensates: it defines the valid insight_type values, marks topic as platform_compare-specific, specifies date_range format with an example and a warning against non-object input, and gives mode-specific defaults for min_frequency and top_n. This is exactly the information an agent needs 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 identifies the tool as a unified data insight analysis tool and enumerates three distinct analysis modes (platform_compare, platform_activity, keyword_cooccur), which gives it a recognizable resource and scope. It is distinguishable from specialized sibling tools by being explicitly multi-mode, though 'data insights' remains somewhat broad.

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 clearly explains what each insight_type does and which parameters apply to which mode, providing strong internal usage context. However, it does not state when to prefer this tool over sibling alternatives such as analyze_topic_trend or analyze_sentiment, and no exclusions are given.

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