calc.engineer

percentage increase

percentage_increase

Calculate the percentage change between an old value and a new value. Determines whether the change is an increase or decrease, the absolute change, and the percentage change. Essential for financial analysis (stock price changes, revenue growth), scientific measurements (before/ after experiments), performance benchmarks, and population statistics. A positive percentage indicates growth; negative indicates decline. Division by zero (old_value = 0) is handled gracefully.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`new_value`	Yes	The new or ending value
`old_value`	Yes	The original or starting value

Output Schema

TableJSON Schema

Name	Required	Description
`change`	Yes	The signed difference (new_value - old_value)
`is_increase`	Yes	True if new_value > old_value, false otherwise
`absolute_change`	Yes	Absolute value of the change
`percentage_change`	Yes	Percentage change from old to new value

TDQS

A4.3/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Explains positive/negative percentage meaning, handles division by zero gracefully, and describes output components (increase/decrease, absolute change).

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?

Concise, front-loaded with core purpose, efficient sentences without waste. Every sentence adds value.

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 tool's simplicity, description covers purpose, behavior, error handling, and use cases. Presence of output schema reduces need to explain return values.

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 clear parameter descriptions. Description adds minimal extra semantics beyond restating old/new value roles, which is already in 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?

Description clearly states it calculates percentage change between old and new values, specifying increase/decrease, absolute change, and percentage. Distinguishes from financial or scientific contexts.

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?

Lists specific use cases like financial analysis, scientific measurements, performance benchmarks, and population statistics. Provides clear context but does not explicitly exclude alternatives or compare to sibling tools.

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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