power_for_two_proportion_test

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the statistical purpose but does not explain what the output contains (e.g., whether it returns the power value, effect size, or critical value), nor any side effects or computational limits. The tool is likely a pure calculation without side effects, but the description doesn't confirm this.

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 very concise at one sentence, but for a statistical tool with four parameters and an output schema, it is under-specified. A second sentence explaining the output or parameter constraints would be warranted. Still, it is not verbose, so it scores well on brevity.

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 that this is a moderately complex statistical tool with 4 parameters, no annotations, and no description of the output schema (though one exists), the description is incomplete. It fails to explain the statistical context (e.g., that this is a two-sided test by default?) or how to interpret the return value. The output schema likely provides structure, but the agent needs to know the tool computes power, not sample size, which it does state.

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?

Despite 0% schema description coverage, the description only mentions n_per_group, p1, and p2 implicitly ('n observations per group', 'difference between two proportions'). The alpha parameter and its default value are not mentioned at all, and no guidance is given on valid ranges for proportions (0-1) or sample size (positive integer). The description adds minimal value over the parameter names, leaving the agent to guess semantics.

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 this tool computes statistical power for detecting a difference between two proportions with a given sample size per group. The verb 'power' combined with 'detect a difference between two proportions' makes the purpose specific and distinct from sibling tools like two_proportion_z_test which would compute the test itself.

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 does not explicitly guide when to use this tool versus alternatives. For example, it doesn't mention that this is a pre-experiment planning tool (as opposed to two_proportion_z_test for analyzing collected data) or that the user also has sample_size_for_two_proportion_test for solving the reverse problem. The context is implied but no direct comparison or exclusion criteria are given.

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