plan_sample_size

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 discloses that the calculation uses a normal approximation and that the result is a close lower bound. This transparency about the method and its limitations is valuable for an agent deciding whether to trust the output.

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 two sentences with no redundant words. It front-loads the core purpose and then adds necessary detail. Every sentence contributes value.

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 tool's complexity (5 parameters, no output schema), the description explains the purpose, effect size mapping, and a crucial caveat. It does not describe the return value explicitly, but for a power analysis tool, the output (sample size) is implied. It is reasonably complete for an agent to use correctly.

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?

With schema description coverage at 0%, the description must compensate. It explains that effect_size uses Cohen's d for two_means/paired_means, Cohen's h for two_proportions, and correlation r for correlation. This adds meaning beyond the schema. However, it does not clarify alpha, power, or two_sided beyond their defaults, though these are standard statistical parameters.

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: 'A priori power analysis: the required sample size for a target power.' It uses a specific verb ('compute') and resource ('sample size'), and it distinguishes from sibling tools like check_assumptions or interpret_result by focusing on planning.

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 usage guidance by noting the normal approximation and advising to confirm exact numbers in G*Power for t-based tests. This tells the agent when to be cautious. However, it does not explicitly state when to use this tool versus alternatives like recommend_test.

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