paired_t_test

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

No annotations are provided, so the description must fully disclose behavioral traits. It only states the tool performs a test, but doesn't mention assumptions (e.g., normality of differences), return values (p-value, test statistic), or potential edge cases. This is insufficient for a statistical test 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 a single sentence of 18 words, front-loaded with the core purpose. It is efficient and easy to parse, though it sacrifices some necessary detail. It earns its place but could be slightly expanded without losing conciseness.

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 (statistical test with 3 parameters, no output schema, no annotations), the description is incomplete. It omits parameter explanations, assumptions, return value details, and usage context. The example helps but does not provide enough for an agent to use the tool 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?

With 0% schema description coverage, the description must compensate by explaining what 'a' and 'b' represent (the paired samples). It does not mention any parameters at all, leaving the agent to infer from the vague title 'A' and 'B'. This fails to add meaning beyond the bare 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 tests whether the mean difference between paired observations is zero, using the example 'before/after on the same subjects'. This verb-resource pair is specific and differentiates from sibling tools like 'two_sample_t_test' (independent groups) and 'one_sample_t_test' (single sample).

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 implies when to use the tool (paired data) via the example 'before/after on the same subjects', but provides no explicit guidance on when not to use it or how it compares to alternatives like 'two_sample_t_test' or 'one_sample_t_test'. The context is clear but lacks exclusions or direct comparisons.

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