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. It discloses the behavior of each analysis mode and important constraints (e.g., date_range must be an object, not integer), but it does not mention error conditions, prerequisites, or potential side effects. It covers core functionality but not edge cases.

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 Args, Returns, and Examples sections, front-loading the purpose. It is somewhat lengthy but every section adds essential information; the examples, while not strictly necessary, are valuable for clarifying usage patterns.

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 multi-mode complexity, no annotations, zero schema descriptions, and presence of an output schema, the description provides a solid outline of modes, parameters, and examples. It lacks details on error handling and return structure specifics, but the output schema covers return values, making the description reasonably complete.

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 0% description coverage, but the description fully compensates by explaining every parameter: insight_type with enumerations and purpose, topic with applicability, date_range with format and example, min_frequency default, and top_n default. Practical usage examples further clarify parameter interactions.

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 the tool is a unified data insight analysis tool integrating multiple analysis modes, and it enumerates the three specific modes (platform_compare, platform_activity, keyword_cooccur). While it names the resource and verb, it could more sharply differentiate itself from sibling analysis tools like analyze_topic_trend or analyze_sentiment.

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 context by defining each insight_type and providing examples, which shows when to use each mode. However, it does not explicitly state when to choose this tool over alternatives or provide exclusions, leaving the agent to infer usage from mode descriptions.

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