Moltline Data Desk

Ab Test

ab_test

Read-onlyIdempotent

Run a two-proportion A/B significance test with a plain-language verdict. FREE.

Typical input {"conversions_a": 120, "visitors_a": 2400, "conversions_b": 156, "visitors_b": 2380} returns {"rate_a_pct": 5.0, "rate_b_pct": 6.55, "relative_lift_pct": 31.1, "z_score": ..., "p_value": ..., "significant_at_95": true, "verdict": "B beats A — statistically significant"}.

Use when exactly two variants each have a trial count and a conversion count. Not for continuous outcomes such as revenue per user, and not for three or more variants. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need visitors > 0 and 0 <= conversions <= visitors"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description
`visitors_a`	Yes	Visitors in variant A; must be at least 1.
`visitors_b`	Yes	Visitors in variant B; must be at least 1.
`conversions_a`	Yes	Conversions in variant A; 0 or more, at most visitors_a.
`conversions_b`	Yes	Conversions in variant B; 0 or more, at most visitors_b.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds detail: 'Every call is read-only and idempotent' and explains error handling behavior ('never raises a protocol error — it returns {"error": ...}'), which is valuable context beyond the 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?

The description is tightly written with three short paragraphs: purpose, example, usage/errors. Every sentence adds value, and the most critical information is front-loaded. No wasted words.

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?

Given the tool's simplicity (4 integer parameters, statistical test), the description covers purpose, input format, output format, usage boundaries, error behavior, and idempotency. The presence of an output schema further supports completeness.

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

Parameters4/5

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

Schema coverage is 100% with detailed descriptions. The description adds a typical input JSON example and explains the constraint 'conversions ≤ visitors' via the error message example. This provides concrete usage context, though the schema already covers the basics.

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 begins with 'Run a two-proportion A/B significance test with a plain-language verdict,' which is a specific verb+resource. It also distinguishes itself from sibling tools like cohort_retention, funnel_report, etc., which are not A/B test tools.

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

Usage Guidelines5/5

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

Explicitly states when to use ('exactly two variants each have a trial count and a conversion count') and when not to use ('Not for continuous outcomes... not for three or more variants'). This provides clear exclusions and implies alternatives.

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

A4.8/5.0

Disambiguation5/5

Each tool addresses a distinct analytical task: A/B test, cohort retention, correlation, CSV profiling, forecasting, funnel analysis, and growth rates. There is no overlap, and the descriptions clearly state when to use each and what not to use it for.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive noun_noun combinations (e.g., ab_test, cohort_retention, growth_rates). No mixing of conventions or vague verbs.

Tool Count5/5

7 tools is a well-scoped set for a data analysis server. It covers a range of common statistical and data profiling tasks without being overwhelming or too sparse.

Completeness4/5

The tool surface covers core analytical needs: hypothesis testing, retention analysis, correlation, data profiling, forecasting, funnel analysis, and growth rates. Minor gaps exist (e.g., no general descriptive statistics beyond CSV profiling, no regression), but the set feels intentional and sufficient for typical data desk queries.

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