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clamp-sh

Clamp Analytics MCP Server

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cohorts.compare

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Compare multiple cohorts side-by-side on retention to see which group retains better, such as comparing signup weeks or experiment variants.

Instructions

Compare 2–10 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?", "is this experiment cohort behaving differently than control?", or stack a few quarters' cohorts to spot a structural retention trend — without composing the rates manually.

Order matters: the first name is treated as the primary; downstream renderings (dashboard, summaries) read deltas relative to it. Duplicates are silently deduplicated.

Examples:

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

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

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

  • "three-arm experiment retention" → names="control,variant_a,variant_b"

  • "is signup retention improving quarter-over-quarter" → names="signups_q1,signups_q2,signups_q3,signups_q4"

Limitations: at most 10 cohorts per call. The same retention windows are applied to every cohort — there's no way to use different windows per slot. Sample-size caveats apply per cohort; check each size value before reading rate deltas (small cohorts make differences look meaningful when they aren't). Stacking many cohorts increases the multiple-comparisons risk — a divergent-looking row in a 5-way compare may just be random variation; commit to a hypothesis before reading.

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.
namesYesComma-separated cohort names, 2–10 entries. First name is treated as the primary. Example: "signups_apr_14,signups_apr_07" or "control,variant_a,variant_b".
periodsNoComma-separated retention windows, same format as cohorts.retention.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cohortsYes
Behavior5/5

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

The description discloses key behavioral traits: order matters (first is primary), duplicates are deduplicated, retention windows are uniform across cohorts, and sample-size caveats apply. Annotations indicate readOnlyHint=true, aligning with the read-only nature of comparing cohorts. No contradiction.

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

Conciseness4/5

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

The description is thorough and well-structured, starting with purpose, then use cases, examples, and limitations. While it is somewhat lengthy, every sentence is informative and earns its place. A slightly more concise summary could improve readability, but it remains effective.

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 availability of an output schema, the description does not need to detail return values. It covers all necessary aspects: when to use, parameter semantics, ordering, limitations, and examples. The tool is complex but the description is complete for an agent to invoke correctly.

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

Parameters5/5

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

Input schema coverage is 100%, and the description adds significant value beyond schema definitions. It explains the 'names' parameter with comma-separated format and ordering, 'periods' with reference to cohorts.retention, and 'project_id' with conditional usage guidance. Examples illustrate parameter combinations.

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's purpose: 'Compare 2–10 saved cohorts side-by-side on retention.' It identifies the specific verb (compare), resource (saved cohorts), and aspect (retention), distinguishing it from siblings like cohorts.retention (single cohort) and traffic.compare (traffic).

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?

Explicit usage contexts are provided via examples and use cases (e.g., 'did April 14 signups retain better than April 7'). Limitations (max 10 cohorts, same windows, sample-size caveats, multiple-comparisons risk) and ordering behavior are clearly stated, guiding the agent on when and how to use this tool versus 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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