normality_test

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

With no annotations, the description carries the burden of disclosing behavior. It explains that p < 0.05 means the data is NOT normal, and it lists method constraints (sample sizes). This provides an understanding of the test's interpretation and operating assumptions, going beyond a mere 'tests normality.' However, it doesn't mention how missing values or non-numeric data are handled, which would add further 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 and front-loaded with the primary action, followed by method details, usage context, and an illustrative example. Every sentence adds value, with no fluff or redundancy. The structure flows logically from purpose to method to interpretation.

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's moderate complexity and the presence of an output schema, the description covers all necessary aspects: what it does, how to choose a method, when to apply it, and how to interpret results. The example ties everything together. It does not need to explain return values since an output schema exists. The description is sufficiently complete for an agent to select and invoke the tool correctly.

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 coverage is 0%, so the description must compensate. It clearly describes the 'column' parameter as a numeric column and explains the 'method' parameter with options ('shapiro', 'ks', 'dagostino') and their suitability. However, the 'df_name' parameter is not mentioned at all, leaving its purpose ambiguous. The example shows usage but does not clarify df_name's role, so compensation is incomplete.

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 begins with a clear, specific verb and resource: 'Test if a numeric column follows a normal distribution.' It distinguishes this tool from sibling statistical tests (like ttest_independent or anova_test) by focusing on normality. The method list and example further clarify its purpose.

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 explicitly states when to use the tool: 'Use before deciding on parametric vs non-parametric tests.' It also provides method selection guidance based on sample size (e.g., 'shapiro' for n < 5000, 'dagostino' for n >= 20). While it doesn't name specific alternatives, this is clear contextual guidance for using the tool appropriately.

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