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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

check_bearing_fault_peak_tool

Detect specific bearing fault frequencies in stored vibration signals using bearing ID, RPM, and tolerance.

Instructions

Check for a specific bearing fault frequency in a stored signal.

    Args:
        signal_id: ID of the stored signal.
        bearing_id: Bearing designation (e.g. '6205').
        fault_type: Which fault to check: 'BPFO', 'BPFI', 'BSF', or 'FTF'.
        rpm: Shaft speed in RPM.
        tolerance_pct: Frequency matching tolerance (default 5%).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_idYes
bearing_idYes
fault_typeYes
rpmYes
tolerance_pctNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_idYesSignal identifier used
bearing_idYesBearing designation
fault_typeYesFault type: BPFO, BPFI, BSF, or FTF
expected_frequency_hzYesExpected fault frequency
detectedYesWhether a peak was detected within tolerance
detected_frequency_hzNoActual peak frequency
magnitudeNoMagnitude at detected frequency
deviation_pctNoDeviation from expected (%)
harmonics_detectedNoHarmonics found
confidenceYesConfidence: high, moderate, low, or none
Behavior2/5

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

With no annotations, the description must fully convey behavior. It describes the action but omits effects like error handling, prerequisites (e.g., signal must exist), and whether it is read-only or modifies state.

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?

Description is concise with a one-line purpose followed by an Args list. It is front-loaded and efficient, though the Args section is somewhat detailed for a tool description.

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

Completeness3/5

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

Given presence of an output schema (not shown), return values need not be described. However, missing context includes input constraints (e.g., signal must be loaded, bearing in catalog) and possible error conditions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description explains each parameter meaningfully, including example values for bearing_id and fault_type, and notes default tolerance. This adds value beyond the schema titles alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it checks for a specific bearing fault frequency in a stored signal, with examples of fault types. It is specific but does not explicitly distinguish from sibling tool 'check_bearing_faults_direct', which may be a broader check.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like 'check_bearing_faults_direct' or 'lookup_bearing_and_compute_tool'. Context for selection is missing.

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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