Compute a business/SaaS/finance metric with exact decimal precision. Metrics: ltv, cac, ltv_cac_ratio, payback_period_months, contribution_margin, gross_margin, churn_rate, mrr_growth_rate, arr, break_even_units, nrr, grr, rule_of_40, magic_number. Rates/margins are decimals (0.05=5%). Call list_metrics for schemas.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | ||
| params | Yes | ||
| currency | No | USD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral-disclosure burden. It usefully adds that calculations use 'exact decimal precision' and that rates/margins are decimals (0.05=5%). However, it does not disclose return format, error handling, currency behavior, or what happens with invalid metric names.
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 two sentences, front-loaded with the core action and precision detail, followed by a compact colon-separated metric list and a single pointer to list_metrics. Every sentence earns its place with no wasted words.
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 a nested params object, no output schema, and no annotations, yet the description omits return values, param object details, and metric-specific inputs. The pointer to list_metrics is helpful but shifts essential schema information elsewhere, leaving this description incomplete on its own.
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%, so the description must compensate. It adds the list of valid metric values and the decimal convention, which is helpful, but it does not explain the 'params' object structure or the 'currency' parameter; it merely defers to list_metrics instead of providing the needed 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?
The description opens with a clear verb and resource ('Compute a business/SaaS/finance metric') and enumerates 14 specific metric names, making the tool's scope obvious. It does not explicitly contrast with sibling batch_calculate, but the listed metrics and exact-precision note sufficiently distinguish it from generic calculation tools.
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 usage for the listed metrics and instructs the agent to 'Call list_metrics for schemas,' which is useful navigation but not a true when-to-use statement. It does not mention alternatives such as batch_calculate or state when not to use this tool.
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 calculation domain: business days, SaaS metrics, compound growth, FX, depreciation, IRR, NPV, loans, and batch execution. The only closely related pair (IRR and NPV) is clearly differentiated by descriptions.
Most tools use snake_case, but the pattern is mixed: calculate_metric and list_metrics are verb-first, currency_convert is verb-last, batch_calculate starts with a modifier, and the rest are noun phrases like loan_amortization and net_present_value. This inconsistency could cause minor confusion but remains readable.
11 tools is well within the typical range for a specialized calculator server. Each tool covers a distinct area, and the auxiliary tools (list_metrics, batch_calculate, health_check) support the core set without bloat.
The server covers a broad swath of business/finance calculations: time-value-of-money (NPV, IRR, compound growth), loans, depreciation, currency, business days, and SaaS metrics. Minor gaps exist (e.g., no general unit conversion or statistical functions), but for the stated purpose the surface is complete.