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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

analyze_fft

Convert vibration signals to frequency domain to identify harmonic components and detect machinery faults. Analyzes a specified segment or entire signal, returning dominant peaks and spectrum statistics.

Instructions

    Perform FFT (Fast Fourier Transform) analysis on a stored signal.

    FFT analysis converts the signal from time domain to frequency domain,
    allowing identification of harmonic components and faults that manifest
    at specific frequencies. Requires the signal loaded via load_signal()
    first; the sampling rate comes from the stored signal metadata.

    By default analyzes the LEADING 1.0-second segment (deterministic:
    two identical calls return identical results). Set
    segment_duration=None to analyze the entire signal, or pass
    random_seed to sample a seeded random segment position instead.

    Args:
        ctx: MCP context for user communication
        signal_id: ID of the stored signal (from load_signal).
        max_frequency: Maximum frequency to analyze (default: Nyquist frequency)
        segment_duration: Duration in seconds to analyze (default: leading
            1.0 s). Set to None to analyze the full signal.
        random_seed: Seed for random segment position (default: None =
            deterministic leading segment).

    Returns:
        FFTResult with top peaks, dominant peak, and spectrum stats.

    Raises:
        ValueError: If the signal_id is not loaded, or the stored signal
            has no sampling rate.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_idYes
random_seedNo
max_frequencyNo
segment_durationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_peaksYesTop spectral peaks sorted by magnitude
total_binsYesTotal number of FFT bins computed
num_samplesYesNumber of analyzed samples
rms_spectralYesRMS of the magnitude spectrum
freq_range_hzYes[min_freq, max_freq] of the spectrum
sampling_rateYesSampling frequency (Hz)
peak_frequencyYesDominant peak frequency (Hz)
peak_magnitudeYesDominant peak magnitude
frequency_resolutionYesFrequency resolution (Hz)
Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses deterministic behavior (leading segment), the effect of random_seed, and the requirement for a sampling rate. It also lists ValueError conditions. It does not explicitly state read-only or idempotency, but those are implied by analysis.

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 well-organized with paragraphs and bullet-like explanation, but slightly verbose. However, every sentence adds value, and the structure aids readability. It could be trimmed slightly without losing clarity, but it's not excessive.

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 of FFT analysis with four parameters and an output schema present, the description covers all major aspects: input requirements, parameter defaults, return type (FFTResult), and error conditions. It is complete and self-contained.

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 description coverage is 0%, so the description must compensate. It adds meaning to all four parameters: signal_id from load_signal, max_frequency defaults to Nyquist, segment_duration defaults to leading 1.0 s, random_seed for random selection. It explains the default behavior and how to use None for full signal.

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 identifies the tool as performing FFT analysis on a stored signal, converting time to frequency domain. It states the specific purpose: identifying harmonic components and faults. This differentiates it from sibling tools like compute_power_spectral_density or compute_spectrogram_stft.

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 explicitly requires the signal to be loaded via load_signal() first, providing a clear prerequisite. It implies usage for harmonic and fault analysis but does not explicitly contrast with alternatives or state when not to use. However, given the sibling list, the context is adequate.

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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