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camelot_ltv_cac_calculator

Idempotent

Calculate customer lifetime value (LTV) against acquisition cost (CAC) and payback periods using ARPU, churn rate, and gross margin inputs.

Instructions

LTV to CAC Calculator: Analyze customer lifetime value against acquisition cost and payback periods.. Local browser processing. Canonical URL: https://www.camelotwebtools.com/tools/ltv-cac-calculator

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cacNoCustomer Acquisition Cost (CAC)
arpuNoAverage Monthly Revenue Per User (ARPU)
churnRateNoMonthly or annual customer churn percentage rate
grossMarginNoGross margin percentage rate

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYesExecution status of the tool (success or error)
prefillUrlYesDirect deep-link URL to view, edit, and export in Camelot Web Tools
canonicalUrlYesCanonical web URL for this tool on camelotwebtools.com
documentTitleYesTitle of the generated document, report, or calculation output
documentMarkdownYesComplete structured output and body formatted in clean Markdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already supply idempotentHint=true, destructiveHint=false, and readOnlyHint=false. The description adds 'Local browser processing,' which is a meaningful behavioral detail about where computation occurs. However, it does not explain output shape, assumptions, or whether results are estimates, so the added value beyond annotations is moderate.

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 short, front-loaded with the core purpose, and includes only a small amount of useful metadata (local processing, canonical URL). The double period is a minor typo, but there is no meaningful 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?

For a simple calculator with fully documented parameters and an output schema, the description is largely complete: it states the analysis focus, mentions payback periods, and adds processing-location context. It could be improved with usage guidance or formula notes, but the agent has enough to invoke it correctly.

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 every parameter has a meaningful description and default value. The tool description adds no parameter-level detail, so the baseline of 3 applies.

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

Purpose4/5

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

The description clearly identifies this as an LTV-to-CAC calculator that analyzes lifetime value against acquisition cost and payback periods. The verb 'Analyze' plus the specific metric pairing distinguishes it from the many generic sibling calculators, though it does not explicitly name an alternative.

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

Usage Guidelines2/5

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

No guidance is given about when to choose this tool over alternatives such as churn_rate_forecaster or pricing_tier_calculator. There are no usage conditions, exclusions, or context cues beyond the tool's name and input fields, leaving the agent to infer suitability.

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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