compute_effect_size

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

With no annotations, the description carries the full burden. It usefully discloses the dispatch model (one calculator selected by name) and the parameter set each calculator consumes, but says nothing about error behaviour for invalid calculator/parameter combinations, unit expectations, or what is returned beyond a standardised effect size.

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?

One front-loaded purpose sentence followed by a compact per-calculator signature table; each line carries information an agent needs. Minor penalty for a stray line break inside the opening sentence and for the table being the only body content, with no grouping of related calculators.

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 zero annotation coverage, no output schema, and an untyped nested parameter object, the description gives enough to pick a calculator but not enough to call it reliably: types, optionality, mutual exclusivity of equal_n with n1/n2, and failure modes are all absent.

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 0% and `parameters` is a free-form object, so the description must supply everything, and it does supply the full calculator dispatch table with the required keys for each. It still omits value types, whether equal_n is a boolean, and optionality/defaults for n1/n2 and correlation, so it falls short of complete.

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?

States a specific verb and resource: converting raw descriptive statistics into a standardised effect size for a test. The calculator table makes the scope concrete. It does not, however, say how it relates to power_analysis or whether that sibling also derives effect sizes, leaving some overlap ambiguous.

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?

Usage is only implied: 'the effect size a test needs' suggests you call this when you have descriptives but need an effect size before a power calculation. There is no explicit statement of when to use this versus power_analysis, or what to do if an effect size is already known.

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