calculate_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 mentions 'Returns minimum sample size' but does not disclose any behavioral traits like read-only or destructive nature. However, as a calculation tool, this is minimal but acceptable.

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, front-loaded with the purpose, and contains no unnecessary words. It efficiently conveys the essential information.

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?

The description states the return value ('minimum sample size') and the output schema exists. It lacks details on underlying assumptions (e.g., confidence interval formula), but for a simple calculator tool, it is fairly complete.

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 100%, and the description lists the three inputs (population, confidence %, margin of error %) but does not add significant meaning beyond the schema's existing parameter descriptions. Baseline is 3.

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 'Compute required sample size for a survey to hit a target margin of error' and mentions use cases like 'survey design and A/B testing', providing a specific verb and resource. It also distinguishes from siblings by referencing 'list_bundles for related 'math' calculators'.

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 explicitly says 'Use for survey design and A/B testing', giving clear context. It also suggests seeing list_bundles for related calculators, but does not provide explicit exclusions or when-not-to-use scenarios.

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