chi_square_test

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 only states the test purpose and does not disclose behavioral aspects such as required data types (categorical), handling of missing values, underlying assumptions (e.g., expected counts), output structure, or potential errors. It adds little beyond the tool name itself.

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 two sentences plus an example, highly concise and front-loaded. It contains no filler and every sentence earns its place.

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?

An output schema exists, so return values need not be described, but the description omits important statistical context (e.g., assumptions of the chi-square test, handling of small expected counts) and the role of df_name. It is sufficient for a simple invocation but incomplete for a robust statistical tool.

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 compensate. The example chi_square_test(col_a="CargoType", col_b="ProductCode") clarifies that col_a and col_b are column names. However, df_name is not mentioned or explained, and there is no mention that columns should be categorical, which is essential. Partial credit is given for the example.

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 explicitly states the tool performs a Chi-square test of independence between two categorical columns and tests for association or independence. This clearly distinguishes it from other statistical tests in the sibling list (e.g., ttest_independent, anova_test) by specifying categorical columns.

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 provides clear context on when to use: when you want to test association between two categorical variables. However, it does not explicitly mention alternatives or when not to use it (e.g., for continuous variables), and does not note prerequisites like needing a current dataframe. It implies usage but lacks exclusions.

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