Analyze power redundancy configuration for a mission-critical data center.

Calculates UPS module count and sizing, generator count and total capacity, PDU requirements and switchgear feeds, electrical efficiency chain losses, UPS loading percentage, and concurrent maintainability / fault tolerance assessment.

Supports N, N+1, 2N, and 2N+1 redundancy configurations.

Args:

• it_load_kw (number): Total IT load in kW

• redundancy_config (string): "N", "N+1", "2N", or "2N+1"

• ups_module_size_kw (number): Individual UPS module capacity (default: 500 kW)

• generator_size_kw (number): Individual generator capacity (default: 2000 kW)

• ups_efficiency (number): UPS efficiency 0.8-0.99 (default: 0.95)

• pdu_efficiency (number): PDU efficiency 0.9-0.999 (default: 0.98)

• transformer_efficiency (number): Transformer efficiency (default: 0.985)

Returns structured JSON with complete power infrastructure sizing and recommendations.

Examples:

• "Size a 2N UPS system for 3 MW" -> it_load_kw: 3000, redundancy_config: "2N"

• "What do I need for N+1 at 1.5 MW with 750 kW UPS modules?" -> it_load_kw: 1500, redundancy_config: "N+1", ups_module_size_kw: 750

Analyze rack density classification and recommend appropriate cooling strategies.

Classifies rack density (low/medium/high/ultra-high/liquid-cooled), recommends cooling approach, estimates airflow requirements per rack, and flags containment and liquid cooling thresholds.

Args:

• rack_count (number): Total number of racks

• avg_kw_per_rack (number): Average power per rack in kW

• floor_area_sqft (number): Optional white space area for W/sqft calculation

• cooling_type (string): Optional current/planned cooling type

Returns density classification, recommended cooling strategy, airflow estimates, and recommendations.

Examples:

• "I have 200 racks at 8 kW each in 10,000 sqft" -> rack_count: 200, avg_kw_per_rack: 8, floor_area_sqft: 10000

• "Cooling strategy for 50 GPU racks at 40 kW" -> rack_count: 50, avg_kw_per_rack: 40

Assess a data center's Uptime Institute Tier classification based on its infrastructure configuration.

Evaluates power redundancy, cooling redundancy, distribution paths, concurrent maintainability, and fault tolerance against Tier I-IV requirements. Identifies gaps between current infrastructure and target tier.

Args:

• target_tier (number): Target Tier level 1-4

• power_redundancy (string): Current power config ("N", "N+1", "2N", "2N+1")

• cooling_redundancy (string): Current cooling config

• distribution_paths (number): Number of independent power paths

• concurrently_maintainable (boolean): Can maintain without IT impact?

• fault_tolerant (boolean): Automatic fault handling?

• generator_backed (boolean): Generator backup available?

• ups_runtime_minutes (number): UPS battery runtime (default: 10)

• fire_suppression (boolean): Clean agent suppression installed?

• monitoring_system (boolean): BMS/DCIM installed?

Returns target vs achieved tier, gap analysis with severity ratings, uptime expectations, and recommendations.

Examples:

• "Does my N+1 facility qualify for Tier III?" -> target_tier: 3, power_redundancy: "N+1", cooling_redundancy: "N+1", distribution_paths: 1, concurrently_maintainable: false, fault_tolerant: false, generator_backed: true

• "Assess our 2N facility against Tier IV" -> target_tier: 4, power_redundancy: "2N", cooling_redundancy: "2N", distribution_paths: 2, concurrently_maintainable: true, fault_tolerant: true, generator_backed: true

Calculate the total cooling load for a data center facility based on IT load, PUE, and environmental factors.

This tool computes cooling capacity requirements in kW, tons, and BTU/hr, estimates required airflow in CFM, rates the facility's PUE efficiency, and provides actionable recommendations for cooling system design.

Accounts for: IT heat rejection, electrical overhead losses, lighting loads, humidification, and altitude derating above 5,000 ft.

Args:

• it_load_kw (number): Total IT electrical load in kW

• pue (number): Power Usage Effectiveness ratio (1.0-3.0)

• safety_factor (number): Design margin (default 1.15 = 15%)

• lighting_area_sqft (number): Floor area for lighting heat gain

• include_humidification (boolean): Include humidification load

• altitude_ft (number): Site altitude for equipment derating

• design_outdoor_temp_f (number): ASHRAE design day temperature

Returns structured JSON with cooling_load_kw, cooling_load_tons, cooling_load_btu, estimated_airflow_cfm, pue_rating, and engineering recommendations.

Examples:

• "Calculate cooling for a 2 MW data center with PUE of 1.4" -> it_load_kw: 2000, pue: 1.4

• "What cooling do I need for 500 kW at 6000 ft altitude?" -> it_load_kw: 500, pue: 1.5, altitude_ft: 6000

Generate a comprehensive data center commissioning plan following ASHRAE guidelines (Levels 1-5).

Creates a structured commissioning plan with test procedures, durations, milestones, and prerequisites scaled to facility size and tier complexity.

Commissioning Levels:

• L1: Factory Witness Testing

• L2: Component Verification & Startup

• L3: System Functional Performance Testing

• L4: Integrated Systems Testing (load bank, concurrent maintenance, fault injection)

• L5: Operational Sustainability (seasonal verification)

Args:

• facility_size_kw (number): Design IT capacity in kW

• tier_level (number): Target Tier 1-4 (affects test complexity)

• include_levels (number[]): Which commissioning levels to include [1,2,3,4,5]

• custom_systems (string[]): Optional custom systems list

Returns structured plan with phases, test procedures, durations, milestones, and recommendations.

Examples:

• "Generate a full Cx plan for a 5 MW Tier III facility" -> facility_size_kw: 5000, tier_level: 3, include_levels: [1,2,3,4,5]

• "Just L3 and L4 for a 1 MW Tier II" -> facility_size_kw: 1000, tier_level: 2, include_levels: [3,4]

Optimize cooling infrastructure for GPU/AI workloads. Calculates thermal loads for modern GPU clusters (H100, A100, H200, B200, GB200), recommends cooling strategies (air, rear-door, direct liquid, immersion), and projects energy costs and savings.

Handles the unique thermal challenges of AI/ML deployments: extreme power density (30-120+ kW/rack), liquid cooling CDU sizing, coolant flow rates, and PUE impact analysis.

Args:

• gpu_count (number): Total number of GPUs

• gpu_model (string): "H100", "A100", "H200", "B200", or "GB200"

• rack_count (number): Number of racks housing GPUs

• cooling_type (string): "air", "direct_liquid", "rear_door", or "immersion"

• ambient_temp_f (number): Ambient temperature in °F (default: 95)

• pue_target (number): Target PUE ratio (default: 1.3)

Returns total heat load, per-rack density, cooling strategy recommendation, CDU sizing, coolant flow rates, chilled water plant capacity, annual energy costs, and liquid vs air savings analysis.

Examples:

• "Cool 64 H100 GPUs across 8 racks with liquid cooling" -> gpu_count: 64, gpu_model: "H100", rack_count: 8, cooling_type: "direct_liquid"

• "What cooling do I need for 16 GB200s?" -> gpu_count: 16, gpu_model: "GB200", rack_count: 2, cooling_type: "immersion"

Look up data center engineering reference data including Tier requirements, PUE benchmarks, rack density classifications, and commissioning phases.

Args:

• category (string): One of "tier_requirements", "pue_benchmarks", "rack_density", "commissioning_phases"

• tier (number): Optional — filter tier requirements by specific tier (1-4)

Returns reference data tables for data center engineering decisions.

Examples:

• "What are the Tier III requirements?" -> category: "tier_requirements", tier: 3

• "Show me PUE benchmarks" -> category: "pue_benchmarks"

• "Rack density classifications" -> category: "rack_density"

Size UPS systems and battery plants for mission-critical data center facilities. Calculates UPS module count, battery string sizing, floor space, structural load, and 10-year Total Cost of Ownership comparing VRLA vs Lithium-Ion batteries.

Supports all standard redundancy configurations (N, N+1, 2N, 2N+1) and both battery technologies with lifecycle cost analysis including replacement cycles.

Args:

• critical_load_kw (number): Critical IT load in kW

• redundancy (string): "N", "N+1", "2N", or "2N+1"

• runtime_minutes (number): Required battery runtime (5, 10, 15, or 30)

• battery_type (string): "VRLA" or "lithium_ion"

• ups_efficiency (number): UPS efficiency 0.8-0.99 (default: 0.96)

• growth_factor (number): Design growth margin 1.0-2.0 (default: 1.2)

Returns UPS module sizing, battery string count, energy capacity, floor space requirements, weight estimates, 10-year TCO comparison, and recommendations.

Examples:

• "Size a 2N UPS with 15 min lithium batteries for 2 MW" -> critical_load_kw: 2000, redundancy: "2N", runtime_minutes: 15, battery_type: "lithium_ion"

• "Compare VRLA vs lithium for 500 kW N+1" -> critical_load_kw: 500, redundancy: "N+1", runtime_minutes: 10, battery_type: "VRLA"