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cohorts.compare

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Compare two saved cohorts side-by-side on retention to determine which group retains better. Returns each cohort's size and retention curve over the same period, enabling direct comparison without manual calculation.

Instructions

Compare two saved cohorts side-by-side on retention. Returns each cohort's size and retention curve over the same period set, so you can read "did this week's signups retain better than last week's?" or "is this experiment cohort behaving differently than control?" without composing the rates manually.

Examples:

  • "did April 14 signups retain better than April 7" → a="signups_apr_14", b="signups_apr_07"

  • "are pro-plan signups stickier than free" → a="pro_signups_q2", b="free_signups_q2"

  • "compare two onboarding variants out to 4 weeks" → a="onboarding_v1", b="onboarding_v2", periods="1w,2w,4w"

Limitations: only two cohorts at a time. The same retention windows are applied to both — there's no way to use different windows per side. Sample-size caveats apply per cohort; check both size values before reading the rate delta.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoTarget project ID (e.g. "proj_abc123"). Required when the credential has access to multiple projects. If omitted and only one project is accessible, that project is used automatically. Call `projects.list` to discover available project IDs.
aYesName of the first cohort.
bYesName of the second cohort.
periodsNoComma-separated retention windows, same format as cohorts.retention.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYes
bYes
Behavior4/5

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

Annotations already declare `readOnlyHint: true`. The description adds that it returns each cohort's size and retention curve, and mentions limitations. It does not contradict annotations and provides useful behavioral context beyond what annotations offer.

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 concise and well-structured: purpose first, then examples, then limitations. Every sentence adds value, no redundancy. Ideal front-loading.

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 complexity (4 params, 2 required, output schema exists), the description covers purpose, usage, parameters, and limitations comprehensively. It is complete enough for an agent to select and invoke correctly.

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 all parameters described. The description adds extra value with concrete examples for parameters `a` and `b`, and clarifies `periods` format. This enhances understanding beyond the schema.

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 clearly states the tool compares two saved cohorts side-by-side on retention, with specific verb 'compare' and resource 'cohorts'. It differentiates from sibling tools like `cohorts.retention` by emphasizing comparison of two cohorts.

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?

The description provides explicit when-to-use examples (e.g., comparing retention of different signup dates, experiment cohorts) and states limitations: only two cohorts, same retention windows, sample-size caveats. This gives clear guidance on when to use this tool vs 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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