calc.engineer

wavelength freq

wavelength_freq

Convert between radio frequency and wavelength. Provide either frequency in MHz or wavelength in metres, and get the full set of equivalent values: frequency in MHz and GHz, wavelength in metres, centimetres, millimetres, and feet. Essential for antenna dimensioning, waveguide selection, and quick band identification. The fundamental relationship is lambda = c / f where c is the speed of light (299 792 458 m/s).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`freq_mhz`	No	Frequency in megahertz (MHz)
`wavelength_m`	No	Wavelength in metres

Output Schema

TableJSON Schema

Name	Required	Description
`freq_ghz`	Yes	Frequency in gigahertz (GHz)
`freq_mhz`	Yes	Frequency in megahertz (MHz)
`wavelength_m`	Yes	Wavelength in metres
`wavelength_cm`	Yes	Wavelength in centimetres
`wavelength_ft`	Yes	Wavelength in feet
`wavelength_mm`	Yes	Wavelength in millimetres

TDQS

A4.5/5.0

Behavior4/5

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

No annotations exist, so the description carries full burden. It reveals the conversion formula (lambda = c/f) and lists output units. It could clarify behavior if both parameters are provided, but overall it's transparent.

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-loading the core operation, then detailing inputs/outputs, use cases, and the formula. No extraneous words.

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 existence of an output schema, the description covers all needed context: what to input, what to expect, and why it's useful. It is complete for a conversion tool.

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 coverage is 100% with descriptions for both parameters. The description adds value by explaining the mutual exclusivity and the full set of outputs beyond what the schema provides, raising it above baseline.

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 converts between radio frequency and wavelength with specific units (MHz, metres). It is distinct from sibling tools like unit_convert and dbm_convert, which cover other conversions.

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 explains to provide either frequency or wavelength to get all equivalent values, and gives use cases (antenna dimensioning, waveguide selection). It doesn't explicitly mention when not to use it or alternatives, but context is clear.

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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