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vswr

vswr

Convert between VSWR, return loss, and reflection coefficient — provide any one parameter and get all related impedance-mismatch metrics. Computes VSWR (voltage standing wave ratio), return loss in dB, reflection coefficient (gamma), mismatch loss, and percentage of power reflected vs transmitted. Essential for antenna matching, transmission line analysis, and RF system budgeting. Feeds into link_budget for system-level mismatch accounting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vswrNoVoltage standing wave ratio (>= 1.0, where 1.0 is perfect match)
return_loss_dbNoReturn loss in decibels (positive value, higher is better match)
reflection_coefficientNoReflection coefficient magnitude (gamma), 0 to 1

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
vswrYesVoltage standing wave ratio
return_loss_dbYesReturn loss in decibels (positive)
mismatch_loss_dbYesMismatch loss in decibels
power_reflected_pctYesPercentage of power reflected back toward source
power_transmitted_pctYesPercentage of power delivered to load
reflection_coefficientYesReflection coefficient magnitude (gamma)

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It transparently lists all computed outputs (VSWR, return loss, reflection coefficient, mismatch loss, power percentages) and specifies the input-output rule ('provide any one parameter'). It does not cover error handling or constraints beyond the schema, but overall is clear.

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?

Three sentences packed with essential information: the core conversion, the list of computed metrics, and applications. No wasted words, front-loaded with the most critical action. Ideal conciseness.

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 tool is simple and the description covers conversion behavior and outputs. An output schema is available, so return values need not be explained in depth. However, it omits guidance on what happens if multiple parameters are provided (does it ignore extras or use the first?), which would improve completeness.

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 each parameter having a detailed description including min/max and explanation. The description adds context about the conversion process and that only one parameter is needed, but does not provide additional parameter-specific meaning beyond the schema. Baseline 3 is appropriate.

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 starts with a specific verb ('Convert') and identifies the resource (VSWR, return loss, reflection coefficient) with a clear input-output relationship. It explicitly states the scope ('impedance-mismatch metrics') and differentiates from sibling tools by mentioning its use as input to 'link_budget', making the purpose unmistakable.

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 clearly indicates when to use the tool: 'Essential for antenna matching, transmission line analysis, and RF system budgeting.' It also hints at workflow by stating it 'feeds into link_budget.' However, it does not explicitly state when not to use it or mention alternatives, losing a point.

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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