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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

estimate_rul

Estimate remaining useful life from repeated degradation measurements over time, requiring a statistically significant trend toward the failure threshold before providing a RUL forecast.

Instructions

Estimate Remaining Useful Life from repeated measurements over time.

RUL is only physically meaningful when fitted on a degradation trend
across MULTIPLE measurements of the same machine taken at different
times (days/weeks/months apart). This tool refuses a single
recording or single point — for within-recording screening use
analyze_signal_trend instead.

Two mutually exclusive input routes (both need `timestamps`, one
entry per measurement, strictly increasing, in `time_unit`):
1. `feature_values`: the degradation indicator already measured
   externally (e.g. RMS velocity trended by a data collector).
2. `signal_ids`: one stored signal per measurement session (loaded
   via load_signal); each recording is reduced to a single
   `feature_name` value.

The degradation indicator is assumed to RISE toward
`failure_threshold`. A statistically significant increasing trend
(slope p-value < 0.05) is required before any RUL is computed; a
flat/insignificant series returns status 'no_degradation_trend'
with no RUL number.

Args:
    ctx: MCP context. Unused — see this module's docstring on logging.
    failure_threshold: Indicator value considered as failure, in the
        same units as the feature values. No universal default is
        imposed — but when the indicator is broadband VELOCITY RMS
        in mm/s, the standard choice is the ISO 10816-3:2009 zone
        C/D boundary that assess_severity / get_zone_boundaries()
        reports for the machine's group and support (single source
        of truth — no boundaries restated here).
    timestamps: Measurement times in `time_unit`, strictly
        increasing (e.g. hours since first measurement).
    feature_values: Indicator values, one per measurement
        (mutually exclusive with signal_ids).
    signal_ids: Stored signal IDs, one per measurement session
        (mutually exclusive with feature_values).
    feature_name: Time-domain feature used to reduce each signal
        (default: "rms"). Ignored for feature_values input.
    method: "linear" (default), "exponential", or "kalman"
        (kalman needs approximately uniform measurement spacing).
    time_unit: Label for the time axis; RUL and
        observation_horizon are expressed in this unit.

Returns:
    RULEstimationResult with status, rul (only when estimated),
    fit_r_squared (goodness of fit — NOT a confidence),
    observation_horizon, and a plain-language message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
methodNolinear
time_unitNohours
signal_idsNo
timestampsYes
feature_nameNorms
feature_valuesNo
failure_thresholdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rulNoEstimated remaining useful life in time_unit (only when status='estimated')
methodYesEstimation method used: linear, exponential, or kalman
statusYes'estimated' (RUL computed), 'no_degradation_trend' (no statistically significant trend toward the threshold — healthy outcome, no RUL number), or 'threshold_already_exceeded' (last measurement is at/above the failure threshold).
messageYesHuman-readable explanation of the outcome and its caveats
time_unitYesUnit of timestamps, observation_horizon, and rul
feature_nameYesDegradation indicator tracked (e.g. 'rms')
current_valueYesMost recent measured indicator value
fit_r_squaredNoR-squared of the fitted degradation curve on the observed data. Goodness of fit only — NOT a confidence or probability. None for the kalman method.
trend_p_valueNoTwo-sided p-value of the series' linear slope (None when not computable). The trend gate requires p < 0.05.
estimated_rateNoEstimated degradation rate in feature units per time_unit (linear/kalman)
rul_interval_95No[lower, upper] approximate 95% interval from the delta-method variance (kalman only). Coverage not validated — treat as an order-of-magnitude band.
num_measurementsYesNumber of measurements in the series
failure_thresholdYesIndicator value considered as failure
observation_horizonYesTime span covered by the measurement series (last minus first timestamp), in time_unit. RUL estimates far beyond this horizon are extrapolations with low reliability.
precision_heuristicNoHeuristic in [0,1]: 1 - rul_std/rul, clipped (kalman only). This is a heuristic, NOT a statistical confidence — do not present it as a probability of correctness.
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does so thoroughly. It discloses that the tool refuses single-point input, requires a statistically significant increasing trend (p<0.05), and returns status 'no_degradation_trend' otherwise. It also explains the assumption that the indicator rises toward the failure threshold, defines fit_r_squared as not a confidence measure, and references ISO 10816-3 boundaries externally, leaving no hidden behavior.

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 long but well-structured, with clear sections for purpose, usage restrictions, input routes, behavioral assumptions, parameter details, and return values. Each sentence contributes necessary information given the complexity of the tool (7 parameters, nuanced degradation logic). It could be slightly tightened, but the length is justified and the front-loaded purpose statement ensures immediate clarity.

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 high complexity of the tool, an output schema presence, and no annotations, the description is exceptionally complete. It covers prerequisites (multiple measurements), input alternatives, statistical requirements, parameter semantics, and return behavior (status, rul conditional, observation_horizon). The only missing detail is the exact shape of the output object, but that is covered by the output schema, so the description meets the completeness bar.

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 provides zero parameter descriptions, so the description must compensate fully—and it does. Each parameter is explained with its role, units, defaults, and constraints: failure_threshold in same units as feature values, timestamps strictly increasing, feature_values and signal_ids mutually exclusive, feature_name default 'rms' and ignored for feature_values, method enum with linear default and kalman spacing requirement, time_unit as the label for RUL expression. This adds substantial meaning beyond the raw schema.

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 opens with a clear verb+resource statement: 'Estimate Remaining Useful Life from repeated measurements over time.' It explicitly distinguishes itself from the sibling tool analyze_signal_trend by stating it refuses single-point data, while also specifying that it operates on multi-session degradation trends. This gives the agent a precise understanding of the tool's scope and differentiates it from related tools.

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 when-to-use and when-not-to-use guidance: it refuses a single recording or point and directs the agent to 'analyze_signal_trend instead' for within-recording screening. It also details two mutually exclusive input routes (feature_values vs signal_ids) and notes the condition that kalman needs approximately uniform measurement spacing, giving clear criteria for selecting this tool over alternatives.

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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