heat_pump_savings_calculatorHeat Pump vs Furnace Savings Calculator — Compare a heat pump against electric resistance or a furnace. Enter your heating need, COP, and rate to see yearly running cost, savings, and payback.
| Name | Required | Description | Default |
|---|---|---|---|
| cop | Yes | ||
| heatNeedKWh | Yes | ||
| comparisonCop | Yes | ||
| electricityRate | Yes | ||
| installedCostDelta | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the core behavior (calculating running cost, savings, payback) but does not explain underlying assumptions, units, or limitations (e.g., that results are estimates based on COP). It doesn't contradict any annotations.
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 title, and every word adds value. It is appropriately concise for a calculator tool.
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 no annotations, no output schema, and 0% parameter coverage, the description is incomplete. It fails to explain two required parameters and ambiguously suggests a choice between 'electric resistance or a furnace' while the schema provides only a single comparisonCop value. An agent would not know how to set comparisonCop or installedCostDelta from the description.
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 only names three of the five parameters ('heating need, COP, and rate'), omitting comparisonCop and installedCostDelta. It hints at payback but provides no units, formulas, or clarity on how parameters relate to outputs.
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: 'Compare a heat pump against electric resistance or a furnace' and lists the outputs (yearly running cost, savings, payback). The verb 'compare' and the resource (heat pump vs furnace) distinguish it from sibling calculator 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 gives clear context for when to use the tool—when comparing a heat pump to alternatives—but does not explicitly mention when not to use it or provide alternative tools. It avoids ambiguity about its general purpose, though exclusions are absent.
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.