data_insights

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It does add useful context with the 1.5σ deviation threshold and the warning that column names depend on the actual CSV. However, it does not state whether the operation is read-only, how failures might occur, or what the 'suggestions' are based on, leaving some behavioral ambiguity.

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 a single compact sentence that front-loads the tool's primary outputs and then adds a decisive caveat about CSV column names. There is no filler or redundancy. It could include more parameter guidance, but for what it conveys it is well-structured and easy to parse.

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?

The tool has an output schema, so return-value detail is covered elsewhere, but there are no annotations and no schema-level parameter descriptions. The description leaves the semantics of metric and source unexplained and offers no guidance on when this tool is preferable to sibling analytics tools. An agent is not equipped to select and invoke this tool correctly based solely on the given information.

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%, and the description does not explain what metric or source mean or how they affect the output. The caveat that column names follow the actual CSV is a small hint that metric likely references CSV columns, but it is not a sufficient explanation. With two undocumented optional parameters, the agent cannot confidently construct a correct invocation.

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 states the tool produces automated insights composed of a data overview, anomaly drill-down based on a 1.5σ deviation threshold, and suggestions. It does not use a direct verb like 'generates' or 'returns', but the phrase '自动洞察' makes the operation clear enough. The stated combination of overview, anomaly detection, and recommendations helps distinguish this from sibling analytics tools such as aggregate_stats or query_video_stats.

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 gives no explicit guidance on when to use data_insights instead of alternative tools like aggregate_stats, correlation_analysis, or sql_query. No exclusions, preconditions, or sibling comparisons are provided. An agent would have to infer when this tool is appropriate from its name and high-level output list alone.

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