calc.engineer

cooling btu

cooling_btu

Estimate the cooling load (BTU/hr) for a homelab or server closet based on equipment wattage, room dimensions, insulation quality, and solar exposure. All electrical power converts to heat — this tool calculates equipment heat output, envelope heat gain through walls, and solar gain to produce a total BTU/hr cooling requirement. Recommends AC tonnage, mini-split sizing (rounded to standard 6K BTU increments), and exhaust fan CFM for ventilation-only cooling. Use after power_cost to size cooling for your homelab room.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`insulation`	No	Wall/ceiling insulation quality: poor (uninsulated garage), average (standard drywall), good (insulated interior), excellent (server room with vapor barrier)	average
`total_watts`	Yes	Total power consumption in watts — all power becomes heat
`sun_exposure`	No	Solar heat gain: none (interior/basement room), partial (one exterior wall with window), full (multiple sun-facing windows)	partial
`room_width_ft`	No	Room width in feet
`target_temp_f`	No	Desired room temperature in degrees Fahrenheit
`ambient_temp_f`	No	Ambient temperature outside the room in degrees Fahrenheit
`room_height_ft`	No	Room ceiling height in feet
`room_length_ft`	No	Room length in feet

Output Schema

TableJSON Schema

Name	Required	Description
`ac_tons`	Yes	Cooling capacity needed in tons of refrigeration (1 ton = 12,000 BTU/hr)
`total_btu`	Yes	Total cooling load in BTU/hr (equipment + envelope + sun)
`envelope_btu`	Yes	Heat gain/loss through walls based on room size, insulation, and delta-T
`equipment_btu`	Yes	Heat generated by equipment in BTU/hr (watts * 3.412)
`will_overheat`	Yes	True if total heat load is positive and no cooling is provided
`exhaust_cfm_needed`	Yes	Exhaust fan airflow needed in cubic feet per minute if using ventilation instead of AC
`mini_split_btu_recommended`	Yes	Recommended mini-split size rounded up to nearest 6,000 BTU increment

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description fully bears the burden of transparency. It explains the core principle ('All electrical power converts to heat'), details the calculation components (equipment heat, envelope gain, solar gain), and mentions output recommendations and rounding. This gives the agent a clear understanding of the tool's behavior and assumptions.

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 composed of two efficient sentences with no extraneous information. It is front-loaded with purpose and inputs, then explains the calculation and output, and ends with usage guidance. Every sentence is essential and well-organized.

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 complexity (8 parameters, output schema exists), the description covers the tool's purpose, inputs, calculation logic, and usage order. It does not list output fields explicitly, but that is acceptable since an output schema is present. Minor gaps: no mention of limitations or accuracy expectations, but overall adequate for an informed agent.

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 description coverage is 100%, so parameters are already documented. The description adds value beyond the schema by explaining the overall model and how parameters contribute to the calculation (e.g., 'equipment heat output, envelope heat gain through walls, and solar gain'). This contextualizes the parameters without repeating schema details.

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's purpose: estimating cooling load (BTU/hr) for a homelab or server closet. It specifies inputs (equipment wattage, room dimensions, insulation, solar exposure) and outputs (total BTU/hr, AC tonnage, mini-split sizing, exhaust fan CFM). It distinguishes from siblings by mentioning use after power_cost, indicating a specific role in a workflow.

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 suggests when to use the tool: 'Use after power_cost to size cooling for your homelab room.' This provides sequential context but does not explicitly state when not to use or list alternatives. However, for a specialized calculation tool, this is sufficient guidance.

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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