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Constant noise-figure circles (LNA design)

noise_circles
Read-onlyIdempotent

Generates constant noise-figure circles in the source plane from NFmin, Γopt, and Rn, and optionally evaluates noise figure for a source impedance, aiding LNA input matching.

Instructions

Constant noise-figure circles in the source (ΓS) plane from the noise parameters NFmin, Γopt and Rn — taken from a .s2p noise block (interpolated at frequency) or given explicitly. Optionally evaluates the noise figure for a proposed source impedance/Γ and, if S-parameters are available, the available gain at Γopt (gain/noise trade-off). Use for low-noise amplifier (LNA) input matching.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rnNoEquivalent noise resistance Rn in Ω (not normalized).
z0NoReference impedance (default 50 or the Touchstone value).
nf_dbNoNoise figures (dB) to draw. Default: NFmin + 0.25, 0.5, 1, 2 dB.
deviceNoTwo-port with a noise block (Touchstone) — or give the explicit noise parameters below.
languageNoLanguage of the human-readable summary: 'en' (English) or 'tr' (Türkçe). Defaults to the server setting.
frequencyNoFrequency. Number in SI base units or engineering string, e.g. 2.4e9, '2.4GHz', '915 MHz'
gamma_optNoOptimum source reflection coefficient Γopt.
nf_min_dbNoMinimum noise figure NFmin in dB.
source_gammaNoEvaluate NF for this source Γ.
source_impedanceNoEvaluate NF for this source impedance.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is fully covered. The description adds useful behavioral context — interpolation at frequency, fallback from .s2p noise block to explicit parameters, and the conditional gain evaluation — but does not mention output format or edge cases like NF below NFmin. With annotations doing the heavy lifting, a 3 is appropriate.

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?

Two information-dense sentences that front-load the core computation and then the optional evaluation. Near-optimal, though the parenthetical interpolation/defaults make it slightly dense for a 10-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 10 parameters, full schema coverage, and strong annotations, the description covers the main computational path, the dual input modes (Touchstone vs explicit), and the optional downstream analyses. It lacks return-value guidance, but no output schema exists, so a small gap remains.

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 every parameter (rn units in Ω not normalized, z0 default, nf_db defaults, device/touchstone options, gamma_opt, source_gamma vs source_impedance) is already documented in the schema. The description does not add syntax or format detail beyond noting the .s2p noise block origin, so the baseline 3 applies.

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?

States specific verb (draw/compute constant noise-figure circles), specific domain object (source ΓS plane), and the exact inputs (NFmin, Γopt, Rn). Distinguishes from sibling gain_circles by scope and names the LNA input-matching use case.

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 final sentence 'Use for low-noise amplifier (LNA) input matching' gives clear context, and the description notes the optional evaluation path for a proposed source impedance. It does not, however, explicitly name alternatives like gain_circles or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.