Compare accuracy for two models scored on separate samples with a two-proportion z-test and confidence interval, showing whether the observed gap is statistically real.
Is A's win over B real when each was scored on a different sample? Two-proportion z-test with a CI on the gap.
| Name | Required | Description | Default |
|---|---|---|---|
| n_a | Yes | ||
| n_b | Yes | ||
| alpha | No | ||
| correct_a | Yes | ||
| correct_b | 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 discloses the method (z-test, CI on the gap), which is useful, but says nothing about assumptions such as independence or minimum sample size, whether a continuity correction is applied, what fields come back (p-value? CI bounds?), or how alpha affects results. For a statistical inference tool this leaves significant behavioral gaps.
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?
Two short sentences with no filler, and the motivating question is front-loaded before the method statement. The interrogative opening is slightly indirect but functionally efficient.
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?
With 5 undocumented parameters, no annotations, and no output schema, the description does not do enough: an agent cannot tell from it whether correct_a is a count out of n_a, how alpha is used, or what the response contains. The statistical method alone does not compensate for the missing parameter and return-value context.
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 the parameter titles ('N A', 'Correct A', 'Alpha') are ambiguous on their own — 'correct_a' could be a count or a rate. The description's A/B framing gives a weak hint about the a/b parameter grouping but never explains n_a, correct_a, or what alpha controls.
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 names a specific statistical procedure (two-proportion z-test) and the resource being compared (A vs B on independent samples), plus a CI on the gap. The phrase 'each was scored on a different sample' implicitly separates it from the paired sibling, though it never names that sibling explicitly.
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?
'When each was scored on a different sample' implies the condition that selects this tool over eval_compare_paired, but no alternative is named and no exclusions or prerequisites are stated. Usage is inferable rather than guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.