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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

lookup_bearing_and_compute_tool

Look up bearing specifications, compute fault frequencies, and detect bearing faults from vibration signals in a single call.

Instructions

Look up bearing, compute fault frequencies, and check signal -- all in one call.

    End-to-end bearing analysis: catalog lookup + frequency calculation +
    envelope spectrum fault detection.

    Args:
        bearing_type: Bearing designation (e.g. 'SKF 6205-2RS', '6205').
        rpm: Shaft speed in RPM.
        signal_id: ID of the stored signal.
        tolerance_pct: Frequency matching tolerance (default 5%).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bearing_typeYes
rpmYes
signal_idYes
tolerance_pctNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_idYesSignal identifier used
bearing_idYesBearing designation
rpmYesShaft speed (RPM)
shaft_frequency_hzYesShaft frequency (Hz)
bearing_frequenciesYesCalculated BPFO/BPFI/BSF/FTF (Hz)
fault_checksYesResults for each fault type
overall_assessmentYesSummary assessment text
most_likely_faultNoMost likely fault type if any
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It candidly states the three steps in one call but does not disclose side effects, authentication needs, or error handling (e.g., missing signal_id). This is adequate but not comprehensive.

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 concise, starting with a clear summary line followed by bullet-like arg descriptions. Some arg text is repetitive of the schema (e.g., 'type: string'), but overall it is efficient and front-loaded.

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 the tool has 4 parameters, no enums, and an output schema, the description adequately covers the three phases of the composite operation. It does not explain return values, but the output schema exists for that purpose.

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?

Schema description coverage is 0%, but the description adds meaningful context: 'bearing_type' is exemplified with 'SKF 6205-2RS', 'rpm' is shaft speed, 'signal_id' is stored signal, and 'tolerance_pct' is matching tolerance with default. This compensates for the schema's lack of descriptions.

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 performs three specific actions: look up bearing, compute fault frequencies, and check signal. It is a composite tool that distinguishes itself from siblings like 'calculate_bearing_characteristic_frequencies' which only calculates frequencies, and 'check_bearing_fault_peak_tool' which checks peaks.

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

Usage Guidelines3/5

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

The description implies usage for end-to-end bearing analysis but does not explicitly state when to use this tool versus alternatives. No when-not or exclusion conditions are provided, relying on the composite nature to guide usage.

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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