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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, correctly signalling a safe, self-contained computation. The description usefully adds the payment/allowance model and the no-account/no-API-key fact, which is real context, but says nothing about what the computation returns or its assumptions.

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 opening sentence is duplicated verbatim, wasting the front-loaded position on a repeat. Pricing and example content is jammed together without clear separation, making the definition noisy rather than tight.

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?

There is no output schema, so the description carries the burden of explaining the return value — but it never states whether it returns per-arm or total N, or what power/baseline assumptions produce. For a statistical tool with four required numeric parameters, this leaves a significant gap.

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 100%, so the baseline is 3. The description only echoes the same example values the schema already lists and never explains what baseline_rate (baseline conversion rate?) or min_detectable_effect (relative vs absolute?) actually mean, so it adds no real semantic value over the structured fields.

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 collocated name and title ('Sample Size') plus the illustrative parameter set make it inferable that this computes a required sample size for a test. However, the description's actual prose ('How many observations before this test can answer anything') never names the method (power analysis / two-proportion test) and gives no differentiation from statistical siblings like abtest, clusteredt, or multipletest.

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?

'ask before you spend' hints at pre-experiment use, and the pricing line explains cost. But there is no statement of when to use this versus the many sibling statistical tools, no prerequisites, and no exclusion criteria.

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