calc.engineer

channel utilization

channel_utilization

Estimates Meshtastic or LoRa mesh channel utilization percentage based on node count, message rate, and per-packet airtime. Determines how much of the shared radio channel is occupied and computes the maximum number of nodes before exceeding a configurable duty cycle limit (default 10%). Returns utilization percentage, headroom, and total packet count. Chain from lora_airtime to get airtime_ms input. Essential for Meshtastic mesh deployment planning to avoid channel congestion and packet collisions.

Input Schema

TableJSON Schema

Name	Required	Description
`nodes`	Yes	Number of active nodes in the mesh network.
`airtime_ms`	Yes	Time-on-air per packet in milliseconds. Obtain from lora_airtime tool.
`max_duty_cycle_pct`	No	Maximum acceptable channel utilization percentage. Default 10% is a common Meshtastic guideline.
`messages_per_hour_per_node`	No	Average messages transmitted per hour per node. Includes position beacons and user messages.

Output Schema

TableJSON Schema

Name	Required	Description
`headroom_pct`	Yes	Remaining headroom before hitting the duty cycle limit (can be negative if over limit).
`utilization_pct`	Yes	Current channel utilization as a percentage of total airtime.
`packets_per_hour`	Yes	Total packets per hour across all nodes.
`max_nodes_at_limit`	Yes	Maximum number of nodes before exceeding the duty cycle limit.

TDQS

A4.3/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It mentions estimation and returns, but does not explicitly state no side effects. However, as a calculator, this is acceptable.

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?

Five sentences efficiently convey purpose, inputs, output, usage chain, and context. No redundancy, well-structured.

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 output schema exists, description adequately covers return values and context. It explains the tool's role in deployment planning without missing critical information.

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 has 100% coverage, but description adds value by explaining where airtime_ms comes from and noting default duty cycle. This supplements 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?

Description clearly states the tool estimates channel utilization percentage for Meshtastic/LoRa mesh based on node count, message rate, and airtime. It distinguishes from sibling tools like lora_airtime by focusing on utilization calculation.

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?

Description advises chaining from lora_airtime for airtime_ms input and states it's essential for deployment planning. While it lacks explicit when-not or alternatives, the guidance is clear and helpful.

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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