cramers_v

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

With annotations already setting readOnlyHint and idempotentHint to true, the description adds valuable behavioral context: it returns a float in [0, 1], provides interpretation thresholds for 2x2 tables, and notes that thresholds shift for larger tables. This exceeds annotation coverage without contradiction.

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 appropriately sized, front-loaded with the definition, followed by usage context and interpretation guidance. Every sentence contributes value without redundancy, earning a top score.

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 presence of an output schema and comprehensive annotations, the description covers purpose, usage, and interpretation well. It is complete enough for an agent to select and invoke the tool correctly, with minor gaps around edge cases (e.g., zero rows/columns) being non-essential.

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 100%, so baseline is 3. The description adds meaning by explaining the relationship between chi2_statistic and chi_square_independence, and clarifies that rows/cols come from 'the same table,' which helps disambiguate parameter usage beyond the schema's individual descriptions.

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 defines Cramér's V as an effect size for a chi-squared test, normalized to [0, 1] for comparability across table sizes. It uses specific verbs and resources, distinguishing it from the raw chi-squared statistic and aligning well with its role among statistical sibling tools.

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?

Explicitly instructs when to use: 'Call after chi_square_independence, passing its statistic...' and differentiates from raw chi-squared. While it doesn't explicitly list when not to use it or name alternative effect sizes like cohens_d, the guidance is clear and contextual.

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