test_hypothesis

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

Annotations are absent, and the description provides no behavioral details beyond the verb 'validate'. It does not disclose whether the tool is read-only, destructive, or requires permissions, nor does it explain the statistical methodology or side effects.

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 concise with a single sentence, but it lacks structure (e.g., bullet points or sections) and omits critical details. It is efficient but incomplete.

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 absence of an output schema, zero annotation coverage, and four unparameterized inputs, the description fails to convey essential information about return values, constraints, or expected behavior, leaving the agent underinformed.

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?

With 0% schema description coverage and no explanation in the description, the meaning of parameters like 'hypothesis', 'null_hypothesis', 'confidence_level', and 'sample_size_min' is entirely opaque. The agent cannot infer appropriate values or constraints.

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 validates research hypotheses with data, indicating a statistical hypothesis testing function. However, it does not distinguish it from sibling tools like 'validate_insight_batch' or 'calculate_sample_size', lacking specificity about the method (e.g., t-test, chi-square).

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. There is no mention of prerequisites, context, or when not to use it, leaving the agent without decision support.

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