calc.engineer

lora range estimate

lora_range_estimate

Estimates LoRa maximum communication range by computing the free-space path loss (FSPL) link budget and applying terrain-dependent correction factors. Calculates total link budget from transmit power, antenna gains, and receiver sensitivity, then solves the FSPL equation for distance after subtracting terrain losses (0 dB open to 50 dB indoor). Returns estimated range in km, link margin at 1 km, and effective path loss. Useful for Meshtastic deployment planning and coverage mapping. Terrain corrections are empirical estimates; real-world range varies with elevation, foliage, and weather.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tx_dbm`	No	Transmit power in dBm. Typical LoRa: 14-30 dBm depending on region and module.
`terrain`	No	Terrain type for path loss correction. open: line-of-sight, suburban: light buildings, urban: moderate buildings, dense_urban: city center, indoor: inside buildings.	suburban
`freq_mhz`	No	Carrier frequency in MHz. Common: 868 (EU), 915 (US/AU), 433 (Asia).
`rx_gain_dbi`	No	Receive antenna gain in dBi. 2.15 dBi = half-wave dipole.
`tx_gain_dbi`	No	Transmit antenna gain in dBi. 2.15 dBi = half-wave dipole.
`rx_sensitivity_dbm`	No	Receiver sensitivity in dBm. Typical LoRa SF10/BW125: -130 dBm. Varies by SF and bandwidth.

Output Schema

TableJSON Schema

Name	Required	Description
`max_range_km`	Yes	Estimated maximum range in kilometers accounting for terrain correction.
`margin_at_1km_db`	Yes	Link margin at 1 km distance in dB. Positive means signal above sensitivity.
`effective_path_loss_db`	Yes	Maximum allowable path loss in dB (link budget minus terrain correction).

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description discloses the calculation method, empirical nature of terrain corrections, and caveats about real-world variation. It adds behavioral context beyond what the input schema provides.

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), front-loaded with the main purpose, and structured logically without redundant 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 full schema coverage, no required parameters, and the existence of an output schema, the description adequately explains the tool's functionality, inputs, and output (range, margin, loss) without needing further detail.

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 description coverage is 100%, so the schema already documents all parameters. The description adds overall context about the link budget equation but does not significantly enhance semantics beyond schema defaults.

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 estimates LoRa maximum communication range by computing free-space path loss link budget with terrain corrections, distinguishing it from siblings like lora_sensitivity or lora_airtime.

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 mentions usefulness for Meshtastic deployment planning and coverage mapping, providing context. It does not explicitly state when not to use or compare with alternatives, but the context is clear enough for selection.

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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