power_for_two_sample_t_test

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

No annotations are provided, so the description carries the full burden. The description only states the computation's purpose—nothing about side effects (it's a read-only calculation, presumably safe), assumptions (equal n, two-tailed? default alpha already specified in schema but not mentioned as one- or two-tailed), or constraints (e.g., effect_size_d must be positive? n_per_group must be integer?). Lacks disclosure of any behavioral traits beyond the core math.

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 sentence, 15 words—efficient and front-loaded with the key concept. It includes the essential elements (power, Cohen's d, n, two-sample t-test) with no filler. Could be marginally improved by adding a verb like 'Calculate' at the start for clarity, but as-is it's concise.

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, 3 parameters (0% schema coverage), and an output schema present (but hidden from this context), the description covers the input semantics adequately but misses return-value context. It doesn't describe what the output represents (probability of detection? range 0-1?), which the output schema might handle, but with no annotations, the description should hint at typical output. Also no mention of one-tailed vs. two-tailed defaults, or whether it returns power or other diagnostics. Adequate but incomplete for a statistical tool.

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% (the JSON schema has zero descriptions for parameters), but the description names all three parameters and binds them: effect_size_d (Cohen's d), n_per_group (per group), alpha (significance level). This provides meaning beyond the raw schema. However, alpha defaults to 0.05 but is not explicitly described as optional, and effect_size_d constraints aren't specified. Given the baseline of 4 for 0% coverage and 3 params, this scores a 4.

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 'Statistical power to detect a given Cohen's d with n observations per group, using a two-sample t-test.' This identifies the verb ('calculate power'), resource ('two-sample t-test'), and key inputs (Cohen's d, n per group). It distinguishes from siblings like power_for_two_proportion_test and sample_size_for_two_sample_t_test. However, it lacks a concise verb phrase like 'Calculate' or 'Compute' at the start, and doesn't explicitly say it is for planning an experiment, which slightly reduces clarity.

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 implies usage when planning a two-sample t-test: given effect size and sample size, estimate power. No explicit guidance on when to use this vs. alternatives (e.g., sample_size_for_two_sample_t_test for reverse calculation, power_for_two_proportion_test for proportions, or why this is appropriate for balanced designs). No mention of prerequisites (e.g., assuming equal variance or normality). This is adequate but not instructive.

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