Calculate the monthly revenue cost of SaaS churn from MRR, churn rate, and customer count. Returns the dollar value leaking out each month.
| Name | Required | Description | Default |
|---|---|---|---|
| mrr | Yes | Monthly recurring revenue (USD). | |
| customers | No | Active customer count (optional, for per-customer view). | |
| churn_rate | 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 present, but description fully discloses that the tool performs a calculation and returns a dollar value. No hidden behaviors or side effects.
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?
Two concise sentences with no redundant information. Front-loaded with action and result.
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?
For a simple calculator with no output schema, the description sufficiently explains return value. Could specify unit or formula, but adequate.
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 coverage is 100%, so description adds minimal value beyond schema. It mentions grouping of parameters but does not provide additional semantics.
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?
Description clearly states the tool calculates monthly revenue cost of SaaS churn using MRR, churn rate, and customer count. It specifies output as dollar value, distinguishing it from siblings like get_ltv 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?
Description implies usage when MRR and churn rate are known, but does not provide explicit when-to-use vs alternatives or when not to use.
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.
