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 of behavioral disclosure. While it mentions the test type and purpose, it doesn't describe key behavioral aspects: what the tool returns (test statistic, p-value, degrees of freedom), whether it performs validation (e.g., checking for non-negative frequencies, sample size adequacy), or any computational characteristics (e.g., handling of small expected frequencies). For a statistical test tool with zero annotation coverage, this is a significant gap.

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, efficient sentence that front-loads the core purpose. The domain/category tags add context without verbosity. However, it could be slightly more structured by separating usage notes from the core description, but overall it's appropriately concise.

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 complexity of a statistical test (3 parameters, no output schema, no annotations), the description is incomplete. It lacks details on parameter semantics, return values, assumptions, and behavioral traits. While it identifies the test type, it doesn't provide enough context for an agent to use it correctly without external knowledge of chi-square tests.

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, so parameters 'observed', 'expected', and 'alpha' are undocumented in the schema. The description only vaguely references 'observed frequencies' and 'expected frequencies' without explaining their format (arrays of numbers), relationships (same length), or meaning (counts or proportions). It doesn't mention 'alpha' at all. With low schema coverage, the description fails to compensate adequately.

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 the tool's purpose: 'Perform chi-square goodness-of-fit test to test whether observed frequencies match expected frequencies.' It specifies the statistical test type (chi-square goodness-of-fit), the action (perform test), and the goal (test match between observed and expected frequencies). However, it doesn't differentiate from potential statistical testing siblings like 'fishers_exact_test' or 'proportion_test' that might also handle categorical data comparisons.

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 some implied usage context through the domain/category tags ('Domain: statistics, Category: inference'), suggesting it's for statistical inference tasks. However, it lacks explicit guidance on when to use this specific test versus alternatives like proportion tests or exact tests, nor does it mention prerequisites (e.g., sample size requirements, independence assumptions) or typical scenarios for application.

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