stats_test

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

With no annotations provided, the description carries the burden. It explains that the tool performs hypothesis tests and requires type-specific fields, but it does not disclose potential side effects, error behavior, or that it is a pure computation. This is average for a calculator-like tool.

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 compact and well-structured with 'When:', 'Params:', and 'Example:' sections. Every sentence adds value, and the example is a useful illustration without redundancy.

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 15 parameters, no annotations, and an existing output schema, the description covers the core usage (test types, required fields, alternative, pooled) sufficiently. However, it omits some details like acceptable alternative values and exact field combinations for every test, leaving minor gaps.

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 0%, but the description compensates by mapping type to required fields (e.g., data/sigma/mu0, x/n/p0, lists for ANOVA). It does not fully document every parameter (e.g., data2, x1/x2), but the type-driven approach provides practical guidance beyond the schema's raw field names.

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 'STAT hypothesis tests' and lists the specific test types (z_test, t_test, etc.), making the tool's purpose explicit. It directly distinguishes from siblings by saying 'not descriptive stats_1var / regression stats_2var'.

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 'When:' header explicitly defines when to use this tool and excludes two sibling tools. The 'Params:' section gives test-specific parameter guidance, and the example provides a concrete invocation, making usage clear.

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