qlty_univariateStatistics

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention that the tool is a read-only query, any side effects, or performance implications. The description merely states the function without behavioral context.

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 concise, using a clear list for arguments and returns. It is front-loaded with the purpose. No redundant information.

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 lack of output schema and low parameter description, the description is incomplete. It does not specify the type of statistics returned, error conditions, or data type constraints. Users are left guessing the tool's full capabilities.

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 description lists the three parameters but adds no semantics beyond the input schema. With 0% schema description coverage, the description fails to explain how parameters affect the result or what univariate statistics include (e.g., mean, median, count).

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 retrieves univariate statistics for a table, specifying the required arguments (database_name, table_name, column_name). However, it does not differentiate from sibling tools like qlty_columnSummary or qlty_standardDeviation, which may also provide column-level statistics.

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?

No guidance is given on when to use this tool versus alternatives such as qlty_columnSummary or qlty_standardDeviation. The description lacks any context about when to invoke this tool preferentially.

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