Project SaaS revenue 12 months forward from current MRR and a monthly growth assumption. Returns month-by-month MRR.
| Name | Required | Description | Default |
|---|---|---|---|
| months | No | Projection horizon in months (default 12). | |
| current_mrr | Yes | Starting monthly recurring revenue (USD). | |
| monthly_growth | Yes | Net monthly growth rate as a decimal (0.08 = 8%/month). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It accurately states the projection direction and output but omits important details like modeling assumptions (constant growth), handling of negative growth, or edge cases.
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 one sentence of 16 words, very concise. However, it could be slightly clearer with better structure, e.g., specifying the default horizon explicitly.
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?
Given there is no output schema, the description should more fully describe the return format. It only says 'month-by-month MRR' without specifying if it's an array or object. The complexity is moderate, so more completeness is warranted.
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 schema already fully describes the three parameters (100% coverage). The description mentions current_mrr and monthly_growth but adds no new semantic information beyond what the schema provides.
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 projects SaaS revenue forward from current MRR and a growth assumption, and returns monthly MRR. This distinguishes it from sibling tools get_churn_cost and get_ltv, which deal with different metrics.
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 does not provide any guidance on when to use this tool versus alternatives. No prerequisites, limitations, or context for when it is appropriate are mentioned.
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.
