calc.engineer

lora sensitivity

lora_sensitivity

Calculates LoRa receiver sensitivity from spreading factor, bandwidth, and noise figure using the Semtech SX1276 datasheet SNR thresholds. Computes the noise floor from thermal noise density (-174 dBm/Hz), channel bandwidth, and receiver noise figure, then adds the spreading-factor-dependent minimum demodulation SNR. Returns sensitivity in dBm, noise floor, required SNR, and thermal noise reference. Essential for link budget planning in LoRaWAN and Meshtastic networks. Feeds sensitivity_dbm to link_budget and lora_range_estimate for end-to-end coverage analysis.

Input Schema

TableJSON Schema

Name	Required	Description
`sf`	No	LoRa spreading factor (7-12). Higher SF = better sensitivity but slower data rate.
`bw_khz`	No	LoRa channel bandwidth in kHz. Lower bandwidth = better sensitivity.
`noise_figure_db`	No	Receiver noise figure in dB. Typical LoRa radio NF is 6 dB (SX1276).

Output Schema

TableJSON Schema

Name	Required	Description
`noise_floor_dbm`	Yes	Receiver noise floor in dBm, computed from thermal noise, bandwidth, and noise figure.
`required_snr_db`	Yes	Minimum SNR required for LoRa demodulation at the given spreading factor.
`sensitivity_dbm`	Yes	Receiver sensitivity in dBm. The minimum signal power for successful demodulation.
`thermal_noise_dbm`	Yes	Thermal noise power density at room temperature: -174 dBm/Hz.

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It details the computation steps (noise floor calculation, adding SNR thresholds) and lists the return values (sensitivity, noise floor, required SNR, thermal noise). This is transparent for a pure calculation 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 concise (5 sentences) and well-structured: it starts with the core function, explains the computation, lists outputs, provides usage context, and mentions integration with other tools. Every sentence adds value.

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?

Given the tool has 3 parameters, defaults, and an output schema (implied by context), the description covers the calculation logic, output fields, and practical use cases. It is complete for a physics-based calculator tool.

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?

Input schema has 100% coverage with descriptions for each parameter. The tool description reiterates the inputs but does not add new per-parameter semantics beyond what the schema provides. With full schema coverage, a baseline score of 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?

The description clearly identifies the tool's function ('Calculates LoRa receiver sensitivity') and specifies the inputs (spreading factor, bandwidth, noise figure) and the underlying model (Semtech SX1276). It distinguishes itself from sibling tools by providing a precise purpose.

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 states that the tool is essential for link budget planning in LoRaWAN and Meshtastic networks and mentions it feeds into 'link_budget' and 'lora_range_estimate'. This provides clear usage context. However, it does not explicitly exclude alternative tools or scenarios, missing some guidance.

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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