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ApplianceIQ MCP Server

estimate_annual_energy_cost

Read-onlyIdempotent

Estimate annual electricity cost for any home appliance, returning low, average, and high ranges from typical U.S. consumption data. Enter your electricity rate ($/kWh) for a more accurate estimate.

Instructions

Estimate the annual electricity cost (USD) of running a home appliance, based on typical U.S. consumption data. Returns a low/average/high range. Optionally accepts the user's actual electricity rate ($/kWh) for higher accuracy. Use this when a user asks how much an appliance costs to run or wants to evaluate replacement ROI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kwh_rate_usdNoElectricity rate in USD per kWh. Defaults to U.S. average ($0.16/kWh).
appliance_typeYesAppliance type. Supported values: refrigerator, freezer, dishwasher, washer, dryer, oven, range, microwave, hvac, furnace, ac_central, water_heater_tank, water_heater_tankless, dehumidifier.
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds behavioral details beyond the schema, such as returning a range and optionally accepting a custom rate for higher accuracy, which is useful context for setting expectations.

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 two sentences, front-loaded with the main function, then covers usage and optional inputs. Every sentence is purposeful with 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?

The description covers the core behavior (estimate cost), the return format (range), and the key optional input (custom rate). Without an output schema, it correctly discloses what the caller gets. It could mention data source limitations, but for this simple estimation tool it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters, including the default for kwh_rate_usd. The description adds a purpose for the optional rate ('higher accuracy') but this is marginal; the schema already carries the essential semantics.

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 estimates annual electricity cost for an appliance in USD, which is a specific verb+resource. It provides the output format (low/average/high range) and distinguishes itself from sibling tools that cover recalls, lifespan, maintenance, and repair/replace decisions.

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 explicitly states when to use the tool: 'Use this when a user asks how much an appliance costs to run or wants to evaluate replacement ROI.' It gives clear context but does not mention alternatives or exclusions, though siblings are quite different in purpose.

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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