mortgage_overpayment_calculatorMortgage Overpayment Calculator — See exactly how much interest and time you save by overpaying your mortgage. Instant amortization schedule plus a clear breakdown of the months you save.
| Name | Required | Description | Default |
|---|---|---|---|
| principal | Yes | ||
| termYears | Yes | ||
| extraMonthly | Yes | ||
| annualRatePct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It mentions producing an 'instant amortization schedule' and a 'clear breakdown of months saved,' but does not explain assumptions, edge cases, or input requirements. This adds some value but not exhaustive.
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 a single, direct sentence that front-loads the key benefit and output. It avoids unnecessary jargon and is well structured.
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 four-parameter calculator with no output schema, the description sufficiently conveys the main outputs (amortization schedule, months saved, interest/time savings). It lacks explicit parameter definitions, but the overall purpose and output are clear enough.
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%, and the description does not describe any of the four parameters. The term 'overpaying' hints at extraMonthly, but principal, annualRatePct, and termYears are unaddressed, leaving the agent to infer from names.
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 the tool's function: it calculates interest and time savings from mortgage overpayment, and provides an amortization schedule. This distinguishes it from sibling calculators like mortgage_refinance or loan_amortization.
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 the tool is for comparing a standard mortgage payment to one with an extra monthly amount, and shows the savings. It does not explicitly name alternatives or state when not to use it, but the context is clear.
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.
Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.