calc.engineer

solar roi

solar_roi

Calculates solar system return on investment, payback period, and levelized cost of energy (LCOE). Models year-by-year savings accounting for panel degradation, utility rate inflation, federal Investment Tax Credit (ITC), state rebates, and annual maintenance. Outputs net cost after incentives, payback year, total lifetime savings, ROI percentage, and LCOE in cents/kWh. Essential for residential and commercial solar financial analysis, installer proposals, and comparing solar vs. grid economics over a 25-year system lifetime.

Input Schema

TableJSON Schema

Name	Required	Description
`system_size_kw`	Yes	System size in kilowatts (kW)
`federal_itc_pct`	No	Federal Investment Tax Credit percentage, default 30% (US ITC)
`system_cost_usd`	Yes	Total installed system cost in USD
`state_rebate_usd`	No	State or local rebate amount in USD, default 0
`annual_production_kwh`	Yes	Estimated annual energy production in kWh (from PVWatts or system_size_kw * peak_sun_hours * 365 * 0.80)
`system_lifetime_years`	No	System lifetime in years, default 25
`annual_degradation_pct`	No	Annual panel degradation rate, default 0.5% per year
`annual_maintenance_usd`	No	Annual maintenance cost in USD, default $100
`electricity_rate_cents`	No	Current electricity rate in cents per kWh, default 15
`annual_rate_increase_pct`	No	Annual utility rate increase percentage, default 3%

Output Schema

TableJSON Schema

Name	Required	Description
`roi_pct`	Yes	Return on investment percentage over system lifetime
`net_cost_usd`	Yes	Net system cost after federal ITC and state rebates (USD)
`payback_years`	Yes	Number of years to recoup net cost from savings
`total_savings_usd`	Yes	Total cumulative savings over system lifetime (USD)
`lcoe_cents_per_kwh`	Yes	Levelized cost of energy in cents per kWh
`year_1_savings_usd`	Yes	First year net savings (USD)
`year_25_savings_usd`	Yes	Final year net savings (USD), or last year if lifetime < 25
`lifetime_production_kwh`	Yes	Total energy produced over system lifetime (kWh)

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It thoroughly explains what the tool models (year-by-year savings, panel degradation, utility rate inflation, ITC, state rebates, annual maintenance) and outputs (net cost, payback year, lifetime savings, ROI, LCOE). No destructive behavior is expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph of four sentences, well-structured: first sentence states purpose, second details what is modeled, third lists outputs, fourth gives usage context. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (10 parameters, output schema exists), the description is complete. It explains all key modeling aspects, provides a formula hint for annual_production_kwh, and lists outputs. The presence of an output schema means return values need not be detailed further.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the formula for annual_production_kwh (from PVWatts or system_size_kw * peak_sun_hours * 365 * 0.80) and overall model context, helping users understand parameter relationships beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates solar ROI, payback period, and LCOE. It specifies what it models (degradation, inflation, ITC, rebates, maintenance) and outputs. This distinguishes it from sibling tools like solar_sizing or solar_load_audit, making purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context: 'Essential for residential and commercial solar financial analysis, installer proposals, and comparing solar vs. grid economics.' It implies when to use but does not explicitly exclude alternatives or state when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation4/5

Despite 89 tools, each has a clearly distinct purpose with detailed descriptions that often reference related tools. Overlap exists (e.g., multiple LoRa/RF tools), but the descriptions are sufficient to distinguish them. Some confusion possible among similar-sounding tools like attenuator_pi and attenuator_tee, but the descriptions explicitly compare them.

Naming Consistency4/5

Consistent underscore-separated lowercase naming. Most tools follow a verb_noun pattern (e.g., capacitor_charge, wire_gauge) or noun_noun (power_cost). Minor inconsistencies such as 'bmi_calculator' vs 'solar_sizing' but overall predictable.

Tool Count2/5

89 tools is far too many for a single MCP server. This scope is more appropriate for multiple specialized servers. The sheer number will slow agent selection and increase cognitive load, reducing coherence.

Completeness3/5

Covers many domains (RF, solar, PCB, networking, math, etc.) but lacks depth in some areas (e.g., no three-phase power, no airflow calculations). Some domains have comprehensive coverage (LoRa/Meshtastic), but others feel incomplete for the tool count.

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