hypothesis_testing

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states the return format (markdown report with test statistic, p-value, conclusion), which is useful but does not address whether the tool is read-only, any side effects, data assumptions, or sample size limits. While basic behavior is clear, more detail would improve transparency.

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: two sentences that define purpose, list capabilities, and provide usage guidance. It is front-loaded with the core action and output, then details supported tests, and ends with a clear when-to-use note. No wasted words.

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 tool has no output schema, the description adequately explains the return format (markdown report with test statistic, p-value, conclusion). It covers all supported test types and their data shapes. Missing details like handling of missing data or assumptions, but for a hypothesis testing tool with a clear input schema, this is reasonably 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?

The input schema covers all 5 parameters (100% coverage), but the description adds value by explaining how the 'data' parameter's shape depends on 'testType' and noting that 'variables' is only used for correlation. This clarifies dynamic behavior beyond the schema's static description, justifying a score above the baseline of 3.

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 that the tool runs statistical hypothesis tests, enumerates supported test types (t-tests, correlation, chi-square, ANOVA), and specifies the output (p-value with reject/fail-to-reject decision). It also distinguishes itself from the sibling tool advanced_statistical_analysis, which focuses on descriptive correlation without inference.

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?

The description includes explicit guidance: 'Use this when you need significance; for descriptive correlation without inference use advanced_statistical_analysis.' This directly tells the agent when to use this tool versus an alternative, meeting the highest standard for usage guidelines.

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