calc.engineer

meshtastic range

meshtastic_range

Estimates Meshtastic node communication range using real hardware profiles and firmware channel presets. Combines device-specific TX power and antenna gain (Heltec V3, RAK WisBlock, T-Beam, T-Beam Supreme, Station G2) with firmware modem presets (Long Fast, Long Slow, Very Long Slow, Medium, Short) to compute receiver sensitivity and FSPL-based range with terrain correction. Automatically populates LoRa parameters from device and channel selection, with optional overrides for custom antennas or power levels. Essential for Meshtastic deployment planning and node placement. Returns range, sensitivity, link budget, and the effective radio parameters used.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`cr`	No	Custom coding rate denominator (5-8). Only used when channel_preset is 'custom'.
`sf`	No	Custom spreading factor (7-12). Only used when channel_preset is 'custom'.
`bw_khz`	No	Custom bandwidth in kHz. Only used when channel_preset is 'custom'.
`device`	No	Meshtastic hardware device. Selects default TX power, antenna gain, and noise figure. Use 'custom' to override all radio parameters manually.	heltec_v3
`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
`tx_power_dbm`	No	Override device default transmit power in dBm.
`channel_preset`	No	Meshtastic firmware channel preset. Selects spreading factor, bandwidth, and coding rate. long_fast is the default; very_long_slow maximizes range at the cost of throughput.	long_fast
`antenna_gain_dbi`	No	Override device default antenna gain in dBi. Useful for aftermarket antennas.

Output Schema

TableJSON Schema

Name	Required	Description
`device_name`	Yes	Human-readable device name.
`channel_name`	Yes	Human-readable channel preset name.
`effective_sf`	Yes	Spreading factor used for the calculation.
`max_range_km`	Yes	Estimated maximum range in km accounting for terrain correction.
`tx_power_dbm`	Yes	Transmit power in dBm used for the calculation.
`link_budget_db`	Yes	Total link budget in dB before terrain correction.
`antenna_gain_dbi`	Yes	Antenna gain in dBi used for the calculation.
`effective_bw_khz`	Yes	Bandwidth in kHz used for the calculation.
`rx_sensitivity_dbm`	Yes	Receiver sensitivity in dBm for the selected channel preset.

TDQS

A4.4/5.0

Behavior4/5

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

No annotations exist, so the description carries the full burden. It explains the combination of device-specific TX power/antenna gain, firmware presets, terrain correction, automatic parameter population, and the output (range, sensitivity, link budget, effective parameters). This is thorough behavior disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loading the main purpose. It is efficient but could be slightly improved with structured lists for readability. Still, every sentence adds meaningful information without repetition.

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 complexity (8 parameters, output schema existence), the description covers tool purpose, input logic, output details, and use case. There are no gaps; it is complete for an AI agent to understand when and how to use this 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?

Input schema covers all 8 parameters (100% coverage). The description adds value by explaining that parameters are automatically populated from device/channel selection and that overrides are optional, which clarifies the interaction between 'device'/'channel_preset' and the custom parameters.

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 specifies the verb 'estimates', the resource 'Meshtastic node communication range', and details the combination of hardware profiles and channel presets. It clearly distinguishes from sibling tools like 'lora_range_estimate' by being Meshtastic-specific.

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 'Essential for Meshtastic deployment planning and node placement', providing clear context for use. However, it does not explicitly mention when not to use or list alternatives, though the purpose inherently differentiates from generic LoRa tools.

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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