compare_groups

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

No annotations are provided, so the description carries the full burden. It discloses the statistical methodology (t-test/Mann-Whitney, ANOVA/Kruskal-Wallis), effect sizes, CI, and return value structure. However, it omits assumptions like numeric metric or missing data handling.

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 compact, front-loaded with the core purpose, and follows with terse bullet-like specifications. No redundant sentences.

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 description covers the tool's decision logic and output components, and an output schema exists. It doesn't mention data prerequisites or handling of edge cases, but given the output schema and sibling context, it is adequately complete.

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%, so the description must clarify parameters. It explains the roles of `metric_column` and `group_column` via the comparison logic, but doesn't explicitly describe `source_id`. This adds meaning beyond the bare 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 it performs statistical comparison of a metric column across group levels, with specific test selection for 2 vs >2 groups. This distinguishes it from sibling tools like compare_sources or auto_compare, which focus on different comparison targets.

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?

Provides clear context for when to use this tool (comparing a metric across groups) and specifies conditions within the tool (2 vs >2 groups). However, it doesn't explicitly name alternatives or exclusions relative to sibling tools like auto_compare or compare_sources.

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