qlty_univariateStatistics

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

Annotations already declare readOnlyHint and idempotentHint. The description adds value by explaining that setting 'persist' to True materializes a volatile table and returns the table name, which is a behavioral trait beyond what annotations provide.

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 and well-structured: a clear purpose sentence, followed by usage guidelines, then a bulleted list of arguments. Every sentence is informative and earns its place.

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?

No output schema, but the description lists the statistics computed (min, max, mean, sd, quartiles, percentiles), which covers expectations. It lacks details on return format but is sufficient given the tool's simplicity.

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 100%, so baseline is 3. The description lists arguments with descriptions that mostly mirror the schema but do not add significant new meaning beyond what is already in the input 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 states the verb (calculate) and resource (full univariate statistics for a single numeric column) and distinguishes from siblings by explicitly naming alternatives for different scenarios (e.g., qlty_standardDeviation for just mean/std, qlty_columnSummary for all columns).

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?

Explicitly states when to use (user asks for complete/comprehensive breakdown of one column) and when not to, providing specific alternative tools (qlty_standardDeviation, qlty_columnSummary) for other cases.

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