solar_panel_savings_calculatorSolar Panel Savings Calculator — Estimate how much a rooftop solar system saves on your power bill: enter size, sun hours, cost, and any currently applicable credit to see yearly savings and payback.
| Name | Required | Description | Default |
|---|---|---|---|
| derate | Yes | ||
| systemKW | Yes | ||
| grossCost | Yes | ||
| horizonYears | Yes | ||
| peakSunHours | Yes | ||
| taxCreditPct | Yes | ||
| electricityRate | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that it provides estimates and specifies the computation based on user inputs. Since no annotations are provided, the description adequately conveys the pure calculation behavior, though it does not detail output format or assumptions.
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 sentence with a front-loaded purpose and no unnecessary words. It is efficient and clear.
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 description communicates core purpose and primary outputs but lacks details on all parameters and assumptions. Given the absence of output schema and annotations, more detail would improve completeness, but it is adequate for a simple calculator.
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 zero schema description coverage, the description partially compensates by explaining some inputs (size, sun hours, cost, credit). However, parameters like derate and horizonYears are not explained, leaving gaps.
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 estimates solar savings on power bills, listing specific inputs and outputs. It distinguishes itself from sibling calculators like solar_system_size_calculator by focusing on savings and payback.
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?
No explicit guidance is provided on when to use this tool versus alternatives. The description implies usage for estimating solar savings but does not mention exclusions or alternative tools.
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.