two_proportion_z_test

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 accurately describes the tool as a hypothesis test (inferring typical test behavior) but does not disclose details like the assumptions (e.g., normality via sample size, independence), what the test computes (z-statistic, p-value), or how it handles edge cases (e.g., zeros). It adds context about 'standard test behind comparing conversion rates' which is helpful but incomplete.

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 a single, compact sentence that efficiently conveys the purpose and a key use case. It is front-loaded with the core purpose. Loses a point because it could be slightly more structured (e.g., separating purpose from example) but overall is appropriately sized.

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 (a hypothesis test with 5 parameters, all required except alpha, and no output schema), the description is adequate for specifying what the test does. However, it does not mention what the tool returns (p-value? z-statistic?), nor does it cover assumptions or preconditions (e.g., independence of groups, sufficient sample size). There is room for improvement to help an agent use the test 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?

Schema coverage is 0%, so the description must compensate. It does not explain the meaning of specific parameters like successes1, n1, successes2, n2, or alpha beyond what their names and types imply. The names are somewhat self-explanatory, but the description misses the opportunity to clarify that successes1/n1 are for group 1 and successes2/n2 for group 2, or that alpha is the significance level. Baseline 3 is appropriate as the description adds limited value over the schema.

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 is for testing whether two independent proportions differ, which is a specific statistical test (two-proportion z-test). It explicitly distinguishes it from siblings like the one-proportion z-test or chi-square tests by focusing on 'two independent proportions' and providing a concrete use case (A/B test conversion rates).

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 this is the standard test for comparing conversion rates between two groups (A/B test), giving users clear context for when to use it. However, it lacks explicit guidance on when NOT to use it or mention of alternatives like chi-square test of independence, which could also apply for proportions in certain situations.

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