Calculate customer lifetime value (LTV) for a SaaS business from ARPU, gross margin, and monthly churn. Uses the canonical David Skok formula.
| Name | Required | Description | Default |
|---|---|---|---|
| arpu | Yes | Average revenue per user per month (USD). | |
| gross_margin | Yes | Gross margin as a decimal (0.85 = 85%). | |
| monthly_churn | Yes | Monthly churn rate as a decimal (0.05 = 5%). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose behavioral traits such as whether it is read-only or destructive, nor any side effects or required permissions.
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?
Single concise sentence front-loading the purpose and formula, but no additional structured sections or examples.
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?
Tool is simple with few parameters; description is adequate but lacks output schema context and fails to explain return values or usage constraints.
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 covers all 3 parameters with descriptions; description adds no new meaning beyond listing input names. Baseline score appropriate given high coverage.
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 it calculates customer lifetime value (LTV) for SaaS, specifying inputs (ARPU, gross margin, monthly churn) and a known formula, distinguishing it from siblings like get_churn_cost and project_revenue.
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?
No guidance on when to use this tool versus alternatives. It mentions 'for a SaaS business' but provides no exclusions or context about when not to use it.
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.
Each tool targets a distinct SaaS metric: churn cost, LTV, and revenue projection. There is no overlap or ambiguity in their purposes.
All tool names follow a consistent verb_noun pattern with snake_case (get_churn_cost, get_ltv, project_revenue), making them predictable and readable.
With 3 tools, this is within the well-scoped range for a focused SaaS metrics server, though it is on the lower end, leaving room for expansion.
The server covers core churn, LTV, and revenue projection, but lacks tools for computing churn rate, customer acquisition cost, or cohort analysis, which are notable gaps for a comprehensive SaaS toolkit.
