calc.engineer

snr margin

snr_margin

Computes LoRa link SNR margin by comparing received power against the noise floor and the spreading-factor-dependent demodulation threshold from the Semtech SX1276 datasheet. Calculates receiver noise floor from thermal noise (-174 dBm/Hz), channel bandwidth, and receiver noise figure. Returns margin in dB and a boolean link-OK indicator. Use to validate whether a LoRa or Meshtastic link will reliably decode packets. Accepts rx_power from link_budget tool output for end-to-end chain analysis.

Input Schema

TableJSON Schema

Name	Required	Description
`sf`	No	LoRa spreading factor (7-12). Higher SF tolerates lower SNR.
`bw_khz`	No	LoRa channel bandwidth in kHz. Affects noise floor.
`rx_power_dbm`	Yes	Received signal power in dBm (e.g., -110). Typically from a link budget calculation or field measurement.
`noise_figure_db`	No	Receiver noise figure in dB. Typical LoRa radio NF is 6 dB (SX1276).

Output Schema

TableJSON Schema

Name	Required	Description
`link_ok`	Yes	True if margin_db > 0 (signal exceeds demodulation threshold).
`margin_db`	Yes	SNR margin above demodulation threshold in dB. Positive = link OK, negative = link failure.
`noise_floor_dbm`	Yes	Receiver noise floor in dBm, computed from bandwidth and noise figure.
`required_snr_db`	Yes	Minimum SNR required for successful LoRa demodulation at the given SF.

TDQS

A4.5/5.0

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 transparently explains the calculation methodology (thermal noise, bandwidth, noise figure, spreading factor, demod threshold from SX1276 datasheet) and what is returned. Lacks mention of edge cases or assumptions, but is sufficient for a calculator tool.

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 (~80 words), front-loaded with the core function, followed by technical detail and usage guidance. Every sentence adds value with no redundancy or fluff.

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 complexity (LoRa SNR margin calculation with multiple parameters and a datasheet reference) and the existence of an output schema, the description covers the calculation steps, inputs, outputs, and integration with link_budget sibling. It provides sufficient context for an AI agent to understand and invoke the tool correctly.

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% (baseline 3). The description adds value by explaining that rx_power_dbm typically comes from link_budget and that sf, bw_khz, and noise_figure_db are used in the calculation, referencing the SX1276 datasheet. This contextualizes parameters beyond schema descriptions.

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 it computes LoRa link SNR margin, describes the calculation method using noise floor and demodulation threshold, and specifies outputs (margin in dB and link-OK boolean). It distinguishes itself from siblings like link_budget and lora_sensitivity by focusing on SNR margin validation for LoRa/Meshtastic links.

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?

Explicitly says 'Use to validate whether a LoRa or Meshtastic link will reliably decode packets' and notes it accepts rx_power from link_budget tool for end-to-end analysis. Provides clear context for when to use, though does not explicitly exclude other scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All calc.engineer tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?