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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

analyze_statistics

Compute essential statistical parameters (RMS, crest factor, kurtosis, peak-to-peak) of a stored signal to screen for potential machinery faults and guide diagnostics.

Instructions

Calculate statistical parameters of a stored signal for diagnostics.

Statistical parameters are key indicators for diagnostics:
- RMS: Effective value, correlated to signal energy
- Crest Factor: Indicates presence of impulses (high = possible faults)
- Kurtosis: Measures impulsiveness (excess kurtosis; >0 = non-Gaussian, >3 = strong impulses)
- Peak-to-Peak: Signal range

Requires the signal loaded via load_signal() first. Statistical
parameters are screening indicators, not definitive diagnostics —
combine with frequency-domain evidence.

**Signal units:** all values are in the signal's native unit. The unit
is reported only when DECLARED — load_signal(signal_unit=...) or the
companion _metadata.json — and never guessed from signal amplitude.
ISO 20816-3 severity tools refuse to produce a verdict until the unit
is declared.

Args:
    signal_id: ID of the stored signal (from load_signal).

Returns:
    StatisticalResult with all statistical parameters

Raises:
    ValueError: If the signal_id is not loaded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rmsYesRoot Mean Square (effective value)
meanYesMean value
peakYesPeak value
std_devYesStandard deviation
kurtosisYesKurtosis (measure of impulsiveness)
skewnessYesSkewness (asymmetry)
unit_noteYesUnit declaration status and how to declare the unit for ISO severity assessment
signal_unitNoDeclared signal unit ('g', 'm/s2', 'mm/s', 'm/s') from companion metadata — never guessed from amplitude. None when not declared.
crest_factorYesCrest Factor (Peak/RMS)
peak_to_peakYesPeak-to-peak value
Behavior5/5

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

With no annotations, the description carries the full burden and excels. It discloses that the signal must be loaded first, that units are only reported when declared (and never guessed), that severity tools need unit declaration, and that a ValueError is raised for unloaded signal IDs. It also explains the diagnostic meaning of each output parameter, going far beyond a basic 'calculate' operation.

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?

The description is well-structured with a clear opening line, a helpful bullet list of statistical parameters, and concise Args/Returns/Raises sections. Every sentence adds value: the parameter explanations inform interpretation, and the unit caveat prevents misuse. It is appropriately sized for a diagnostics tool.

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 single parameter, the presence of an output schema (StatisticalResult), and no annotations, the description is remarkably complete. It covers prerequisites, limitations, unit handling, error cases, and the nature of the results. No critical contextual gaps remain.

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?

The schema only provides type/title for signal_id, but the description adds essential semantics: 'ID of the stored signal (from load_signal)' and explicitly ties it to the load_signal prerequisite. It also states the ValueError condition, clarifying that the parameter must reference a previously loaded signal. This fully compensates for the 0% schema description coverage.

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's function: 'Calculate statistical parameters of a stored signal for diagnostics.' It uses a specific verb (calculate), identifies the resource (statistical parameters of a stored signal), and distinguishes itself from siblings like analyze_fft (frequency-domain) and check_bearing_faults (fault-specific) by focusing on statistical indicators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage context: 'Requires the signal loaded via load_signal() first' and 'Statistical parameters are screening indicators, not definitive diagnostics — combine with frequency-domain evidence.' This states when to use the tool, a prerequisite, and a clear recommendation to pair with alternative frequency-domain analysis, fulfilling the when/when-not/alternatives guidance.

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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