calc.engineer

battery autonomy

battery_autonomy

Calculates how many days a battery bank can sustain loads without solar input — critical for off-grid and backup power sizing. Accounts for depth of discharge, round-trip efficiency (lithium vs lead-acid), minimum state of charge, and optional partial solar contribution during cloudy weather. Outputs autonomy in days and hours, usable capacity, and daily deficit. Use with avg_solar_contribution_pct = 0 for worst-case (no sun) scenarios, or 20-30% for realistic cloudy-day modeling. Chains from solar_sizing (battery_kwh) and solar_load_audit (daily_kwh).

Input Schema

TableJSON Schema

Name	Required	Description
`dod`	No	Depth of discharge (0-1), default 0.8 for lithium
`daily_kwh`	Yes	Daily energy consumption in kilowatt-hours (kWh)
`battery_kwh`	Yes	Total installed battery capacity in kWh
`min_soc_pct`	No	Minimum state of charge percentage, default 20%
`round_trip_efficiency`	No	Battery round-trip efficiency (0-1), default 0.90 for lithium, use 0.80 for lead-acid
`avg_solar_contribution_pct`	No	Average solar contribution during autonomy period (0-100%), default 0 for worst-case

Output Schema

TableJSON Schema

Name	Required	Description
`usable_kwh`	Yes	Usable battery capacity after DoD and efficiency losses (kWh)
`autonomy_days`	Yes	Number of days the battery can sustain loads
`autonomy_hours`	Yes	Number of hours the battery can sustain loads
`daily_deficit_kwh`	Yes	Effective daily energy need after solar contribution (kWh)
`battery_cycles_per_year`	Yes	Estimated full discharge cycles per year (365 / autonomy_days)

TDQS

A4.4/5.0

Behavior4/5

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

No annotations provided, but the description discloses outputs (days/hours autonomy, usable capacity, daily deficit) and accounts for depth of discharge, efficiency, min SOC, and partial solar. It is transparent about assumptions and inputs.

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?

Three sentences with no wasted words. The main purpose is front-loaded. Every sentence adds distinct value (core calculation, parameters, usage advice, chaining context).

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

Completeness4/5

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

Given the presence of an output schema (not shown but signaled), the description adequately covers behavioral context, parameter guidance, and usage scenarios. It could mention that inputs must be positive (schema enforces exclusiveMinimum), but overall complete for a calculator.

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%, yet the description adds value by explaining default dod (0.8 for lithium), round-trip efficiency default (0.9 lithium, 0.8 lead-acid), and usage of avg_solar_contribution_pct. This goes beyond the schema's basic descriptions.

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?

Description begins with a specific verb ('Calculates how many days a battery bank can sustain loads without solar input') and clearly identifies the resource (battery bank autonomy). It distinguishes from sibling tools like battery_charge_time and battery_life by focusing on autonomy under no solar input.

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?

Description provides explicit advice on using avg_solar_contribution_pct (0 for worst-case, 20-30% for realistic) and mentions chaining from solar_sizing and solar_load_audit. It does not explicitly state when not to use, 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.

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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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