cramers_v

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

With no annotations provided, the description must disclose behavioral traits. It only states the formulaic definition and normalization, but does not mention input validation (e.g., negative chi2, non-positive n, rows/cols < 2), the return format, or that it assumes inputs from a chi-square test of independence versus goodness-of-fit. This is insufficient for safe tool invocation.

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 short sentence, making it concise but not structured to be helpful. It contains no sections, examples, or additional context. Every word serves a purpose but the overall utility is limited.

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 that the tool has 4 required parameters with no schema descriptions, no annotations, and an output schema (whose content is unknown), the description is incomplete. It does not explain how to use the tool, what the output represents, or edge cases. An agent would likely need to infer too much.

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 entirely explain the parameters. It does not define what chi2_statistic, n, rows, or cols represent. For example, n could mean total sample size, and rows/cols refer to contingency table dimensions, but this is absent. An agent cannot correctly map real-world values to these parameters without additional context.

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 it is an effect size for a chi-squared test of independence, normalized to [0,1]. This specifies the verb (compute effect size), resource (chi-squared test), and a key property (normalization). However, it does not differentiate from sibling effect size tools like cohens_d or cohens_h, though the context of chi-squared independence is implicit.

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 no guidance on when to use Cramér's V, when not to use it, or how it compares to alternatives such as phi coefficient. There is no mention of prerequisites (e.g., a significant chi-square test) or data assumptions. This leaves the agent without decision-making context.

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