payback_period_calculatorCAC Payback Period Calculator — Calculate your CAC payback period with simple and discounted methods. Find the month your customer acquisition cost is fully recovered, plus a cumulative chart.
| Name | Required | Description | Default |
|---|---|---|---|
| cac | Yes | ||
| arpa | Yes | ||
| targetMonths | Yes | ||
| grossMarginPct | Yes | ||
| annualDiscountRatePct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description partially discloses behavior by mentioning both simple and discounted methods and the cumulative chart output. However, it omits any limitations, assumptions, or details about calculation logic.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, but it redundantly repeats the tool name 'CAC Payback Period Calculator' at the outset, which adds slight repetition without new information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is too sparse for a tool with five required parameters and no output schema. It fails to explain how to populate inputs or what the exact return structure is, making correct invocation difficult.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has five required parameters with 0% description coverage. The description does not explain any parameter meanings (e.g., cac, arpa, grossMarginPct), leaving the agent to infer from names alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates CAC payback period with simple and discounted methods, and specifies the output (month recovered and cumulative chart). This differentiates it from related calculators like cac_ltv_calculator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives, nor any conditions or prerequisites. It simply describes what it does without contextual usage advice.
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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Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.