effect_size_r

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 states the conversion action but lacks behavioral details such as formula used, assumptions (e.g., for t-tests), error handling, or output format. The description is minimal and does not disclose important traits beyond the basic operation.

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 concise and front-loaded, stating the core function in a single sentence. The domain/category information is appended efficiently. There is no wasted text, though it could benefit from more detail without sacrificing brevity.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It lacks details on the conversion method, parameter meanings, output interpretation, and statistical context. For a tool with two required parameters and no structured documentation, the description does not provide sufficient context for reliable use.

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. It mentions 't-statistic' and implies 'df' (degrees of freedom) via context, but does not explain what these parameters represent, their units, valid ranges, or how they relate to the conversion. The description adds minimal semantic value beyond the parameter names.

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: 'Convert t-statistic to effect size r (correlation coefficient)'. It specifies the verb ('convert'), the input ('t-statistic'), and the output ('effect size r'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools, though the domain/category hint helps.

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 this tool versus alternatives. It mentions the domain ('statistics') and category ('inference'), which implies context, but offers no explicit when-to-use rules, prerequisites, or comparisons with sibling tools like 'cohens_d' or other statistical functions.

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