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ChurnLens

by io.github.kindrat86

calculate_ltv

Calculate gross-margin-adjusted customer lifetime value, the LTV:CAC ratio and CAC payback in months. Omitting gross margin yields lifetime revenue rather than lifetime value — usually a third of the number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cacNoCustomer acquisition cost. Optional — supply it to get the ratio and payback.
arpaYesAverage revenue per account, per month.
gross_margin_pctNoGross margin percentage. Defaults to 100, which gives lifetime revenue.
monthly_churn_rate_pctYesMonthly churn, as a percentage.

TDQS

A4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as whether the tool is read-only, has side effects, or requires specific permissions. As a calculator, it is likely non-destructive, but this is not stated.

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 extremely concise: two sentences that front-load the main purpose and provide a critical usage hint. Every part earns its place with no redundancy.

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

Completeness4/5

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

Given the tool's low complexity and full schema coverage, the description sufficiently explains inputs and outputs. It could mention the output format (e.g., numeric values) but is largely complete for its purpose.

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 parameter descriptions. The description adds value beyond the schema by explaining the effect of omitting gross margin (lifetime revenue vs. value) and the optional nature of CAC. This provides meaningful context for parameter usage.

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 calculates gross-margin-adjusted lifetime value, LTV:CAC ratio, and CAC payback months. It distinguishes between lifetime value and lifetime revenue, and the verb 'calculate' plus the specific metrics make the purpose unambiguous.

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 provides clear context on what the tool computes and when to use the optional CAC parameter. However, it does not explicitly state when not to use this tool or suggest alternatives among siblings, which would earn a 5.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct concept (revenue concentration, churn rate, LTV, zombie MRR, scoring bands, composite health). No two tools overlap in purpose.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case (e.g., analyze_revenue_concentration, calculate_churn_rate). No mixed conventions.

Tool Count5/5

6 tools is well-scoped for a specialized churn analytics server. Each tool earns its place, covering key metrics without bloat.

Completeness5/5

The tool surface covers the core churn analytics workflow: concentration, churn rates, LTV, zombie detection, scoring bands, and composite health. No obvious dead ends.

Resources