chi_square_goodness_of_fit

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

With no annotations, the description must carry full behavioral disclosure. It states the test purpose but fails to mention what the tool returns (e.g., test statistic, p-value, decision), how it handles invalid inputs, or any side effects. The absence of output schema exacerbates this 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 sentence, which is concise but excessively minimal. It lacks structure (e.g., bullet points, sections) and does not front-load key distinctions. While it earns its place by stating the basic purpose, it could provide more value in the same space.

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 three parameters, no output schema, and a complex statistical context (plus 16 sibling tools), the description is severely incomplete. It omits return format, assumptions, practical usage hints, and differentiation from similar tests, leaving the agent underinf.

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% and the description adds no explanation of the three parameters (observed, expected, alpha). The agent receives no semantic help beyond raw schema types and defaults, such as that observed and expected arrays must have equal lengths or that alpha is the significance level.

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 identifies the tool as a chi-square goodness-of-fit test, stating it checks if observed category counts match an expected distribution. While it names the specific statistical test, it does not differentiate from sibling chi_square_independence, which tests association rather than distribution fit.

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?

No guidance is provided on when to use this tool versus alternatives like one_sample_t_test or chi_square_independence. Assumptions (e.g., expected frequencies ≥5, independent observations) are omitted, leaving the agent to infer usage context from the name alone.

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