calc.engineer

noise figure cascade

noise_figure_cascade

Calculate the cascaded noise figure of a multi-stage receiver chain using the Friis formula. Each stage has a noise figure and gain in dB. The first stage dominates overall system noise, which is why low-noise amplifiers (LNAs) are placed at the front of the chain. Returns total cascaded noise figure, total gain, and equivalent noise temperature. Feeds into link_budget for complete receive-chain sensitivity analysis.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`stages`	Yes	Ordered array of receiver chain stages, each with nf_db and gain_db

Output Schema

TableJSON Schema

Name	Required	Description
`total_nf_db`	Yes	Total cascaded noise figure in dB
`noise_temp_k`	Yes	Equivalent noise temperature in kelvin (T0 = 290 K)
`total_gain_db`	Yes	Total gain of the chain in dB

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 carries the behavioral burden. It explains the calculation method (Friis formula) and why first stage dominates, disclosing the mathematical behavior. It does not mention any side effects or destructive actions, but for a calculation tool, this is sufficient.

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?

Four sentences, front-loaded with purpose, each sentence adds value: purpose, formula mention, educational context, integration hint. No unnecessary words.

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 one parameter and output schema, the description covers what it does, how, why it works (first stage dominance), and how it fits into a larger workflow (link budget). This is complete and actionable.

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 coverage is 100%, so baseline is 3. The description reiterates that noise figure and gain are in dB but adds no new semantics beyond what the schema already documents for each parameter.

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 calculates cascaded noise figure using Friis formula, specifies the resource (multi-stage receiver chain), and distinguishes from sibling tool link_budget by stating it feeds into that tool for complete sensitivity analysis.

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 explains when to use this tool (as a precursor to link_budget) and provides context about LNA placement. However, it lacks explicit when-not or alternative tool guidance, which would make it even clearer.

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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