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log-wade

datacenter-mcp-server

by log-wade

Power Redundancy Analyzer

dc_analyze_power_redundancy
Read-onlyIdempotent

Size UPS, generator, and PDU systems for data center power redundancy, assessing efficiency losses and fault tolerance for N to 2N+1 configurations.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
it_load_kwYesTotal IT electrical load in kilowatts (kW)
pdu_efficiencyNoPower distribution unit efficiency (default: 0.98 = 98%)
ups_efficiencyNoUPS efficiency at operating load (default: 0.95 = 95%)
generator_size_kwNoIndividual generator capacity in kW (default: 2000 kW / 2500 kVA)
redundancy_configYesPower redundancy configuration: N (none), N+1 (one spare), 2N (fully redundant), 2N+1 (redundant + spare)
ups_module_size_kwNoIndividual UPS module capacity in kW (default: 500 kW)
transformer_efficiencyNoTransformer efficiency (default: 0.985 = 98.5%)
Behavior3/5

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

Annotations already declare this as read-only, idempotent, and non-destructive, so the bar is lower. The description adds value by listing the calculations performed and stating it returns structured JSON, but it does not disclose deeper behavioral details such as error handling, prerequisites, or response format specifics.

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 well-structured with a clear opening purpose, a list of calculations, an Args section, and Examples. Every sentence contributes meaningful information, and the length is appropriate for a tool with seven parameters; there is no fluff or repetition.

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

Completeness3/5

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

The tool has no output schema, and while the description lists many outputs and says it returns structured JSON, it does not specify the JSON keys or structure. For a complex tool without an output schema, more detail about the return format would be needed for full completeness, but the description still gives a solid high-level overview.

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?

The input schema already covers 100% of parameters with descriptions and defaults, so the baseline is 3. The description adds value through its Args list (though largely duplicative) and especially the two concrete examples, which illustrate how to map natural language queries to actual parameter values.

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 analyzes power redundancy configuration for data centers and lists specific calculations (UPS module count, generator capacity, PDU requirements, etc.). This verb+resource combination distinguishes it from sibling tools like cooling load or tier classification.

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?

The description implies usage by focusing on power redundancy analysis and provides two examples demonstrating common scenarios. However, it does not explicitly state when to use this tool versus alternatives or mention exclusions, so the guidance is only implicit.

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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