Level-payment loan: monthly payment, total interest, payoff, and (optional) full schedule. payment = P*r/(1-(1+r)^-n), r=annual_rate/12.
| Name | Required | Description | Default |
|---|---|---|---|
| currency | No | USD | |
| principal | Yes | ||
| annual_rate | Yes | ||
| term_months | Yes | ||
| extra_payment | No | ||
| include_schedule | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It reveals the payment formula and mentions the optional schedule, which is helpful. Yet it omits behavioral details such as handling of zero/negative interest rates, effects of extra_payment, rounding, or output structure.
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 long and front-loads the main outputs. The formula is efficient and adds value without redundancy. Every word earns its place.
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 core calculation is well-covered, but the tool has 6 parameters, no output schema, and no annotations. Missing details about extra_payment behavior, currency handling, and the exact shape of the amortization schedule leave notable gaps for a financial tool.
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?
With 0% schema coverage, the description must explain parameters. It explains the three required parameters (principal, annual_rate, term_months) through the formula, but ignores currency, extra_payment, and include_schedule. This partial compensation is adequate for core use but incomplete for full understanding.
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 identifies the tool as computing monthly payment, total interest, payoff, and an optional full schedule for a level-payment loan. It distinguishes from siblings by specifying the exact financial calculation and formula, making it unambiguous.
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 phrase 'Level-payment loan' and the formula establish clear context for when to use this tool (fixed-rate, equal installment loans). However, it does not explicitly mention alternatives or exclusion scenarios, so it stops short of full when/when-not guidance.
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.