calc.engineer

sales tax

sales_tax

Calculates total cost including sales tax for a purchase. Given a unit price, tax rate percentage, and optional quantity, computes the subtotal (price times quantity), the tax amount rounded to two decimal places, and the final total. Useful for estimating purchase costs across US states and municipalities with different tax rates, comparing pre-tax and post-tax prices, and budgeting. Chain into salary_to_hourly to see how many work-hours a purchase represents.

Input Schema

TableJSON Schema

Name	Required	Description
`price`	Yes	Unit price of the item in dollars (or any currency). Must be positive.
`quantity`	No	Number of items to purchase. Defaults to 1.
`tax_rate_pct`	No	Sales tax rate as a percentage (e.g. 8.875 for 8.875%). Defaults to 0 — enter your local rate.

Output Schema

TableJSON Schema

Name	Required	Description
`total`	Yes	Subtotal plus tax amount.
`subtotal`	Yes	Price times quantity before tax.
`tax_amount`	Yes	Total tax amount, rounded to two decimal places.
`effective_rate_pct`	Yes	The tax rate applied, echoed back for confirmation.

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It explains the computation steps: subtotal, tax rounded to two decimals, final total. It omits potential edge cases (e.g., negative tax rate schema-constrained) but clearly describes the core behavior. No contradictions since no annotations exist.

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 three sentences, front-loaded with the main purpose. It is efficient and contains no superfluous content. Every sentence adds value: purpose, computation, use cases, and chaining suggestion.

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

Completeness5/5

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

Given the existence of an output schema (not shown but present), the description need not explain return values. It covers purpose, parameters, use cases, and even suggests a chaining workflow. It is complete for this tool's complexity.

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 well-described parameters. The description reiterates the parameters (unit price, tax rate, optional quantity) but adds only minor extra context like 'enter your local rate.' The baseline of 3 is appropriate as the description adds limited value beyond the schema.

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 'calculates total cost including sales tax for a purchase,' specifying the verb and resource. It distinguishes itself from the large set of sibling tools (mostly engineering/electronics calculators) by being a financial calculator. The purpose is immediately obvious.

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 provides specific use cases: estimating purchase costs across different tax rates, comparing pre/post-tax prices, and budgeting. However, it does not explicitly state when not to use the tool or name alternative tools. The mention of chaining into salary_to_hourly gives contextual usage 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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