qlty_standardDeviation

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states that it returns a formatted response with query results and metadata, which is vague. It does not mention if the operation is read-only, performance implications, or data type requirements for the column.

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, with a single sentence for purpose followed by a clean list of arguments and return type. No unnecessary words. However, the return type description could be more detailed.

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 complexity of a statistical function and the presence of sibling tools, the description lacks details such as handling of non-numeric columns, null values, aggregation scope, and precise output format. No output schema is provided, so the description should explain more about the return value.

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, so the description adds basic meaning for each parameter (database name, table name, column name). While this clarifies the role of each parameter, the descriptions are minimal and could provide more context like expected data types or format.

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 computes standard deviation from a column in a table. The verb 'get' and resource 'standard deviation' are specific. However, it doesn't explicitly distinguish from sibling tools like qlty_univariateStatistics which may also compute standard deviation as part of a set of 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 usage guidelines are provided. The description does not specify when to use this tool versus alternatives such as qlty_univariateStatistics or other aggregation tools. No conditions or when-not-to-use are mentioned.

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