calc.engineer

lora airtime

lora_airtime

Calculates LoRa packet time-on-air using the Semtech AN1200.13 formula. Computes symbol duration, preamble time, payload symbol count, effective data rate, and the minimum transmission interval for 1% duty cycle compliance. Essential for capacity planning in LoRaWAN and Meshtastic mesh networks. Accepts spreading factor (SF7-SF12), bandwidth (125/250/500 kHz), coding rate (4/5-4/8), payload size, header mode, CRC, and optional low data rate optimization. Feeds airtime_ms to channel_utilization for mesh load analysis.

Input Schema

TableJSON Schema

Name	Required	Description
`cr`	No	LoRa coding rate denominator (5-8). Represents 4/5 through 4/8 FEC ratio.
`sf`	No	LoRa spreading factor (7-12). Higher SF = longer range but slower data rate.
`crc`	No	Whether CRC is appended to payload. Recommended true for reliability.
`bw_khz`	No	LoRa channel bandwidth in kHz. Common values: 125, 250, or 500.
`payload_bytes`	Yes	Application payload size in bytes (0-255).
`explicit_header`	No	Whether to use explicit header mode. True for LoRaWAN; false for implicit header.
`preamble_symbols`	No	Number of preamble symbols. LoRaWAN uses 8; raw LoRa may vary.
`low_data_rate_optimize`	No	Enable low data rate optimization. Auto-calculated if omitted: enabled for SF >= 11 at BW 125 kHz.

Output Schema

TableJSON Schema

Name	Required	Description
`airtime_ms`	Yes	Total packet time-on-air in milliseconds.
`data_rate_bps`	Yes	Effective data rate in bits per second.
`symbol_time_ms`	Yes	Duration of a single LoRa symbol in milliseconds.
`payload_symbols`	Yes	Number of symbols used for the payload + header portion.
`preamble_time_ms`	Yes	Preamble duration in milliseconds (includes 4.25 sync symbols).
`duty_cycle_1pct_interval_s`	Yes	Minimum interval between transmissions in seconds to respect 1% duty cycle regulation.

TDQS

A4.8/5.0

Behavior5/5

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

Since no annotations are provided, the description carries full burden. It discloses the underlying formula, computed intermediate values (symbol duration, preamble time, etc.), parameter influences, and integration with another tool. No contradictions.

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?

Two well-structured sentences that front-load the main purpose and formula, then detail computed outputs and use cases. No extraneous words. Every sentence adds value.

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 presence of an output schema (context signals), description does not need to explain return values. It covers all parameters, their typical values, usage context, and integration with sibling tools. Complete for a calculation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline is 3. Description adds significant value by explaining each parameter's role in the computation, providing typical values (e.g., 'LoRaWAN uses 8' for preamble_symbols), and specifying auto-calculation for low_data_rate_optimize.

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?

Clearly states it calculates LoRa time-on-air using a specific formula (Semtech AN1200.13). Verb 'Calculates' and resource 'LoRa packet time-on-air' are specific. Lists computed outputs and use cases. Differentiates from siblings like lora_param_optimizer by mentioning capacity planning and integration with channel_utilization.

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 states it is 'essential for capacity planning in LoRaWAN and Meshtastic mesh networks' and 'feeds airtime_ms to channel_utilization for mesh load analysis.' Provides clear context for when to use but does not explicitly mention when not to use or alternatives.

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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