calc.engineer

battery life

battery_life

Calculates battery runtime and energy capacity given cell capacity in mAh, nominal voltage, and average current draw. Accounts for real-world discharge efficiency (Peukert-adjacent derating) to produce effective capacity, total energy in Wh, and runtime in hours and days. Useful for IoT sensor node planning, portable device design, UPS hold-up estimation, and solar battery bank cycling analysis. Outputs feed into solar_sizing (battery_kwh) and ups_runtime (battery_wh) for system-level calculations.

Input Schema

TableJSON Schema

Name	Required	Description
`voltage`	No	Nominal battery voltage (V), default 3.7 for Li-ion
`efficiency`	No	Discharge efficiency factor (0-1), accounts for conversion losses and derating, default 0.85
`capacity_mah`	Yes	Battery capacity in milliamp-hours (mAh)
`current_draw_ma`	Yes	Average current draw in milliamps (mA)

Output Schema

TableJSON Schema

Name	Required	Description
`energy_wh`	Yes	Total battery energy in watt-hours (capacity_mah * voltage / 1000)
`runtime_days`	Yes	Estimated runtime in days
`runtime_hours`	Yes	Estimated runtime in hours
`effective_capacity_mah`	Yes	Effective usable capacity after efficiency derating (mAh)

TDQS

A3.7/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It discloses efficiency derating and outputs (effective capacity, energy, runtime). However, does not explain mathematical model limitations or assumption of constant current. Adequate but not thorough.

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, each earning its place: action/inputs, behavior/outputs, use cases. Front-loaded with verb 'Calculates'. No fluff.

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 complexity (4 params, no nested objects, output schema exists), the description covers inputs, outputs, use cases, and downstream tools. It is complete for an agent to understand when and how to use the tool.

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% with descriptions for each parameter. The tool description adds value by explaining the purpose of efficiency, default voltage for Li-ion, and what outputs are produced. This contextualizes parameters beyond schema.

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

Purpose4/5

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

Description clearly states 'Calculates battery runtime and energy capacity' with specific inputs and outputs. It distinguishes from siblings by mentioning integration with solar_sizing and ups_runtime, but lacks explicit contrast with other battery tools like mah_to_wh or battery_autonomy.

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

Usage Guidelines3/5

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

Provides use cases (IoT, portable devices, UPS, solar) and mentions downstream tools. However, no guidance on when not to use or alternatives (e.g., for simple conversion use mah_to_wh).

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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