Microapp

ab-test-sample-size

Read-onlyIdempotent

Required sample size per arm for a two-proportion A/B test, given the expected baseline conversion rate, the minimum detectable effect (relative lift), the significance threshold alpha (default 0.05, two-tailed), and the statistical power (default 0.80).

Input Schema

TableJSON Schema

Name	Required	Description
`alpha`	No	Significance threshold (default 0.05). Two-tailed.
`power`	No	Statistical power 1−β (default 0.80).
`baseline_rate`	Yes	Expected control conversion rate as a proportion in (0, 1). E.g. 0.05 for 5%.
`minimum_detectable_effect`	Yes	Smallest relative lift you want to detect, as a proportion. 0.10 means 'detect a 10% relative lift over baseline' (so for a 5% baseline, you want to detect a move to 5.5%).

Output Schema

TableJSON Schema

Name	Required	Description
`alpha`	Yes	Significance threshold used.
`power`	Yes	Statistical power used.
`total`	Yes	Total visitors across both arms: 2 × per_arm.
`per_arm`	Yes	Required visitors per arm (control AND variant each need this many).
`target_rate`	Yes	Target rate after the minimum detectable lift.
`baseline_rate`	Yes	Baseline rate used in the calculation.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

AddedMay 21, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint true, so the description adds value by specifying the statistical assumptions: two-tailed alpha default of 0.05 and power default of 0.80. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One well-structured sentence that front-loads the result and lists inputs efficiently; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only calculator with a rich schema and an output schema, the description provides all necessary context: purpose, key inputs, defaults, and statistical framing. No additional details are needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the description essentially recounts each parameter's meaning from the schema (baseline rate, relative lift, alpha, power). The description adds no parameter-level detail beyond the schema, so a baseline of 3 is warranted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool computes the required sample size per arm for a two-proportion A/B test, naming all key inputs. This specific verb+resource clearly differentiates it from sibling calculators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description establishes clear context: it is for two-proportion A/B test sample-size calculations, with default alpha and power. It does not explicitly name alternatives or when-not-to-use, but the intended use is unambiguous.

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

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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have distinct purposes. However, 'hex-to-rgb' is redundant with 'color-converter', which already handles hex-to-RGB conversion, causing potential confusion.

Naming Consistency4/5

Names follow a consistent lowercase-with-hyphens style, but vary in pattern (e.g., 'angle-converter', 'average-calculator', 'dedup-lines'). One tool ('internal-do-not-call') deviates from the descriptive norm.

Tool Count2/5

With 46 tools, the server is heavily populated. Many converters could be merged into a generic unit converter, and there is redundancy, making the surface unnecessarily large for a single server.

Completeness4/5

The server covers a broad range of utility domains: converters, text processing, math, cryptography, etc. Minor redundancies exist (e.g., hex-to-rgb vs color-converter), but the set is otherwise comprehensive.

Resources

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