sw_sample_size

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

With no annotations provided, the description carries the transparency burden and does well: it discloses the formula, the three behavioral branches, and the pilot behavior of running 20 samples. It does not explicitly state side effects or return format, but since this is a calculation tool and an output schema exists, the disclosure is adequate.

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 structured with a formula, bullet points, and a bolded warning. Every sentence adds value: the formula, three usage modes, the caution about MDE, and the source of std_diff. It is longer than minimal but appropriately detailed for a tool with three distinct operational modes.

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 has an output schema and no annotations, the description covers all needed context: mathematical methodology, all three usage scenarios, the pilot fallback, and a caution about interpreting results. It is complete for the tool's complexity.

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 explains all three parameters: std_diff is sourced from sw_compare_arms paired.std_diff, expected_effect is the target effect size for sample size calculation, and n_current is used to compute minimum detectable effect. It also explains the no-arguments case, making parameter semantics clear.

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 computes required sample size or minimum detectable effect based on statistical parameters, with the opening '算出来的，不是问用户的' and three explicit usage modes. This distinguishes it from sibling tools like sw_plan or sw_compare_arms, which have different purposes.

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?

It provides explicit when-to-use guidance for all three parameter combinations: with std_diff+expected_effect, with std_diff+n_current, and with none. It also tells users to check the minimum detectable effect first and points to where std_diff comes from (sw_compare_arms paired.std_diff), fully covering usage context.

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