test_proportion

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

No annotations are provided, so the description must carry behavioral burden. It discloses the tests performed (normal approximation and exact binomial for one sample, group comparison for multiple) and input modes. However, it does not disclose the output shape, the specific multi-group test used, or handling of continuity/confidence intervals beyond what the schema already provides.

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?

Two sentences, front-loaded with the core purpose, and every clause adds a distinct piece of information: one-sample mode, raw-data mode, and multi-sample mode. No filler or redundant restatement.

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 handles the main usage modes and input styles, and the schema covers all parameter details. However, with no output schema or annotations, the return value and the exact multi-group statistical method remain underspecified, leaving some residual uncertainty for an agent.

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 baseline is 3. The description adds useful framing for x/data/successes/n and group by tying them to one-sample vs group-comparison modes, but it does not add significant semantics beyond the existing per-parameter descriptions.

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?

States a specific verb, 'test', and resource, 'proportions', with concrete scenarios: one-sample against a hypothesized proportion (normal approximation plus exact binomial) and multi-sample group comparison. This clearly distinguishes it from sibling test tools like t-tests or ANOVA.

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?

Provides clear context for when to use one-sample vs two-or-more-sample mode ('pass group to compare') and when to give raw successes/n vs a dataset. It does not explicitly mention when not to use it relative to sibling test_categorical, but the intra-tool usage conditions are strong.

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