calc.engineer

unit convert

unit_convert

Convert between units across 10 categories: length (m, km, mi, ft, in, yd, nmi), mass (kg, lb, oz, g, ton), volume (l, gal, ml, cup, fl_oz), area (m2, ft2, acre, hectare), speed (mps, kph, mph, knot), pressure (pa, psi, bar, atm, mmhg), energy (j, kwh, btu, cal, wh), data (b, kb, mb, gb, tb), time (s, ms, min, hr, day, year), and temperature (c, f, k). Accepts any value with source and target unit abbreviations. Returns the converted result with the formula used.

Input Schema

TableJSON Schema

Name	Required	Description
`to`	Yes	Target unit abbreviation (e.g., 'mi', 'kg', 'c', 'bar', 'btu'). Case-insensitive.
`from`	Yes	Source unit abbreviation (e.g., 'km', 'lb', 'f', 'psi', 'kwh'). Case-insensitive.
`value`	Yes	The numeric value to convert.

Output Schema

TableJSON Schema

Name	Required	Description
`result`	Yes	The converted value.
`formula`	Yes	Human-readable conversion formula applied.
`to_unit`	Yes	Normalized target unit abbreviation.
`category`	Yes	Unit category (length, mass, volume, area, speed, pressure, energy, data, time, temperature).
`from_unit`	Yes	Normalized source unit abbreviation.

TDQS

A4.3/5.0

Behavior3/5

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

No annotations exist, so the description alone must disclose behavior. It mentions returning 'the converted result with the formula used,' which adds transparency, but lacks details on error handling, precision, or edge cases (e.g., unsupported unit combinations).

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 concise: two sentences that front-load categories and units, then explain inputs and outputs. Every sentence is informative with no redundancy.

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 and the presence of an output schema, the description covers all essential aspects: supported categories, units, input structure, and return content (result + formula). It is fully adequate for correct invocation.

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?

Schema description coverage is 100%, but the description adds value by listing unit examples per category and implying case-insensitivity (the schema mentions it). This helps the agent understand valid inputs beyond the bare 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 'Convert between units across 10 categories' and lists specific units, making the tool's purpose unambiguous. It distinguishes itself from sibling specialized calculators (e.g., ohms_law, battery_life) by being a general unit converter.

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 implicitly tells when to use this tool (any unit conversion need) but does not explicitly state when not to use it or name alternatives. However, given the diverse sibling tools, the context makes it clear this is the general-purpose conversion option.

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.

Resources

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