statistical_test

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

With no annotations, the description carries full burden, but it only restates that statistical tests are run and gives their names. It does not disclose how data is grouped, whether assumptions apply, what outputs are produced, or whether the operation is read-only.

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 phrase with no fluff, which serves conciseness, but it is under-specified for a tool with six parameters. It is not structured to convey parameter relationships or usage details.

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?

For a six-parameter tool with zero parameter documentation and no annotations, this description is far too thin. An agent cannot determine how to fill group_a, group_b, group_column, or column for each test, nor what the result format is beyond the existence of an output schema.

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 and the description provides no explanation for table_name, column, group_a, group_b, or group_column. The test enum is self-documenting, but the relationship between tests and required grouping/column parameters is entirely absent.

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's function and enumerates four concrete statistical tests (t-test, Mann-Whitney, KS, Chi-square), making it clear this is a hypothesis-testing tool. It is less than a full sentence with no explicit verb, but the list of tests is specific enough to separate it from broader analysis siblings.

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 about when to choose this tool over closely related siblings such as compare_tables or correlation_analysis, nor any criteria for selecting among the four tests. The description only lists available tests without explaining use context.

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