hypothesis_test

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

No annotations are provided, and the description does not disclose any behavioral traits (e.g., assumptions, error handling, or side effects). For a tool with no annotations, the description carries full burden but fails to inform about what happens with invalid inputs or test results.

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 extremely short (5 words), but it is under-specified rather than concise. Every sentence should earn its place, but this single sentence omits critical details, making it insufficient.

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 complexity of 6 parameters, no schema descriptions, no annotations, and a vague description, the tool definition is incomplete. While an output schema exists, the description does not reference it or indicate what the tool returns.

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?

The input schema has 6 parameters with 0% schema description coverage, and the tool description adds no meaning to any parameter. The phrase 'named statistical hypothesis test' does not clarify what 'test', 'data1', 'data2', 'params', 'alpha', or 'alternative' represent.

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 states it performs a 'named statistical hypothesis test,' which indicates the action and resource. However, it fails to specify what tests are available or how to specify them, leaving ambiguity. It is better than a tautology but not sufficiently clear to differentiate from siblings like 'bootstrap' or 'regression.'

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?

No guidance is provided on when to use this tool versus alternatives such as 'bootstrap' or 'distribution.' The description gives no context about appropriate scenarios, prerequisites, or exclusions.

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