Calculate the smallest accuracy gain over a baseline that an eval with n test cases can reliably detect, enabling you to sanity-check whether an existing eval set is adequately powered.
Smallest accuracy improvement over baseline_acc that an eval with n test cases can reliably detect. Use this to sanity-check an existing eval set.
| Name | Required | Description | Default |
|---|---|---|---|
| n | Yes | ||
| alpha | No | ||
| power | No | ||
| paired | No | ||
| discordance | No | ||
| baseline_acc | 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, and it discloses almost nothing behavioral: not that this is a pure read-only computation, not the statistical assumptions baked into the defaults (alpha=0.05, power=0.8, paired test), and not how the result is expressed (accuracy points vs. proportion). Only the headline behavior is stated.
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 tight sentences with the definition front-loaded and the usage hint second; nothing is padded. The only cost is that brevity here is partly under-specification rather than efficiency.
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?
No output schema, no annotations, and 0% schema coverage for 6 parameters means the description must do the heavy lifting, and it does not: return-value semantics, the paired/discordance interaction, and the effect of alpha/power are all unaddressed.
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% across 6 parameters, and the description only touches the two required ones (n, baseline_acc) by name. The statistically consequential parameters — alpha, power, paired, discordance — are completely unexplained, leaving the agent to guess at semantics that strongly affect the answer.
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?
Names a specific computed quantity (minimum detectable accuracy improvement) in terms of two named inputs, which is precise enough for an agent to recognize the tool's role in an eval-design toolkit. It does not explicitly contrast itself with eval_sample_size or the compare_* siblings, so the differentiation is left implicit.
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?
'Use this to sanity-check an existing eval set' gives one concrete scenario, which is more than nothing. But there is no statement of when NOT to use it (e.g., to plan a new eval, use eval_sample_size instead) and no mention of the sibling tools at all.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.