cac_ltv_calculatorCAC : LTV Ratio Calculator — Calculate Customer Lifetime Value, LTV:CAC ratio with poor/fair/good/excellent verdict bands, CAC payback months, and monthly gross profit per customer.
| Name | Required | Description | Default |
|---|---|---|---|
| cac | Yes | ||
| arpa | Yes | ||
| grossMarginPct | Yes | ||
| monthlyChurnPct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. The description lists the outputs (LTV, ratio, payback, gross profit) and mentions 'verdict bands', but lacks details on assumptions or calculation methodology (e.g., how LTV is derived). It adds some value beyond the name, so a 3 is appropriate.
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 a single, focused sentence that lists all key outputs without redundancy. It is front-loaded with the tool name and purpose, making it highly efficient and easy to parse.
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 tool has 4 required parameters, no output schema, and no annotations. The description lists the computed metrics but does not explain the formulas or edge cases, which would be helpful for correct usage. It is adequate but not comprehensive for a calculator with this complexity.
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?
Schema description coverage is 0% and the description provides no explanation for parameters like ARPA, grossMarginPct, monthlyChurnPct, or CAC. The names are suggestive but not defined. The description should compensate for the low schema coverage but does not, leaving the agent to infer meanings.
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's purpose: calculating Customer Lifetime Value, LTV:CAC ratio with verdict bands, CAC payback months, and monthly gross profit. The verb 'Calculate' and specific resource 'Customer Lifetime Value' distinguish it from sibling calculators, though it doesn't name a specific sibling.
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 implies when to use this tool (when needing LTV/CAC metrics) and the context is clear, but it does not explicitly mention alternatives or exclusions among the many sibling calculators. It provides no 'use instead' guidance, hence a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
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.