power_analysis

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

No annotations, so the description carries the full disclosure burden. It usefully says the return includes both numbers and ready-to-paste prose, but says nothing about validation behavior (which parameter combinations are invalid for a given test) or what happens when required inputs are missing.

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?

Front-loaded with the one-line purpose, then a dense, well-mapped enumeration of solve_for modes and the minimum input requirements. Every line carries information; the slightly ragged line-wrapping is cosmetic, not bloat.

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?

For a 14-parameter computational tool with no annotations and no output schema, the description covers the core decision surface (solve_for semantics, sample-size input rules) and even previews the return shape. It falls short only on the 'test' value domain and allocation_rule/allocation_ratio disambiguation.

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, and it does so well for solve_for, n/n1/n2, predictors and df. However alpha, power, tails, effect_size, allocation_rule vs allocation_ratio (an ambiguity an agent will hit), beta_alpha_ratio and especially the 'test' string domain are left undocumented.

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 ('Run one power analysis') and says what comes back ('full numeric result plus ready-to-paste prose'). It implicitly separates itself from power_curve and compute_effect_size by framing itself as the single-shot analysis, but never names those siblings.

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 solve_for block is effectively a when-to-use table for each mode (a priori, post hoc, criterion, sensitivity, compromise), and it tells the caller when to pass n1+n2 versus n(+allocation_ratio). What's missing is explicit routing against siblings, e.g. 'use power_curve for a range of values'.

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