Compare two models on the same test set by running an exact McNemar test on paired outcomes and returning a p-value plus a confidence interval for the accuracy gap.
Is A's win over B on a shared test set statistically real?
n: total cases; a_only: A right & B wrong; b_only: B right & A wrong;
both: both right. Returns exact McNemar p-value and a CI on the gap.| Name | Required | Description | Default |
|---|---|---|---|
| n | Yes | ||
| both | Yes | ||
| alpha | No | ||
| a_only | Yes | ||
| b_only | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It usefully discloses that the p-value is exact and that a CI on the gap is returned, but says nothing about what alpha controls, error conditions (e.g., inconsistent n), or determinism.
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?
Very compact and front-loaded: the motivating question comes first, then the count semantics, then the return values. The question form is slightly indirect as a purpose statement but costs almost nothing.
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?
For a computation tool with no annotations and no output schema, the description covers inputs and outputs reasonably but omits the alpha parameter and any statement of result shape (statistic vs CI bounds). An agent would still need to guess how alpha changes the output.
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% and all titles are just field names, so the description does real work: n, a_only, b_only and both are defined precisely in terms of correctness outcomes. Only alpha is left undocumented, and the relationship between n and the three counts is not stated.
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 frames the tool as an exact McNemar test on a shared test set, which is a specific verb+resource (paired comparison of A vs B). 'Shared test set' implicitly separates it from eval_compare_unpaired, though the sibling is never named.
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?
Usage is implied by the pairing framing and the 'A's win over B' scenario, but there is no explicit when-to-use/when-not guidance and no pointer to eval_compare_unpaired or eval_sample_size as alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.