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LGDiMaggio

mcp-server-mcsa

by LGDiMaggio

run_full_diagnosis

Perform full MCSA pipeline on current signals to identify rotor, stator, and bearing faults, delivering a complete diagnostic report.

Instructions

Run a comprehensive MCSA diagnostic analysis on a current signal.

Performs the full pipeline: preprocessing → spectrum → fault detection for broken rotor bars, eccentricity, stator faults, and optionally bearing defects. Returns a complete diagnostic report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_idNoID of a stored signal (from generate_test_current_signal or load_signal_from_file). Preferred over raw array.
signalNoRaw time-domain current signal. Use signal_id instead for large signals.
sampling_freq_hzNoSampling frequency in Hz. Auto-resolved when using signal_id.
supply_freq_hzNoSupply frequency in Hz
polesNoNumber of poles
rotor_speed_rpmNoRotor speed in RPM
bearing_defect_freq_hzNoBearing defect frequency in Hz (optional, for bearing analysis)
tolerance_hzNoFrequency search tolerance in Hz

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description outlines the pipeline stages (preprocessing → spectrum → fault detection) and lists the specific fault types analyzed. This provides good behavioral transparency beyond a basic purpose statement. However, it does not mention prerequisites, side effects (e.g., data storage), or authorization needs, which are not covered by annotations (none provided).

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 concise, with three sentences that front-load the main purpose, then detail the pipeline and optional bearing analysis, and finally state the output format. Every sentence adds value without redundancy.

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's complexity (8 parameters, many siblings, output schema exists), the description is adequate. It conveys the pipeline and fault types covered. However, it could hint at the preferred parameter (signal_id over signal) and describe the report contents more explicitly, though the output schema may cover that.

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

Parameters3/5

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

The input schema has 100% description coverage for all 8 parameters, so the baseline is 3. The description adds no additional parameter-specific meaning beyond the schema, such as clarifying that signal_id is preferred over signal or how tolerance_hz is used.

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 explicitly states the tool runs a comprehensive MCSA diagnostic analysis on a current signal and lists the pipeline steps (preprocessing, spectrum, fault detection for specific faults). This clearly distinguishes it from sibling tools like detect_broken_rotor_bars or compute_spectrum, which focus on single steps.

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 when a full diagnostic is needed but does not provide explicit when-to-use or when-not-to-use guidance relative to siblings. No alternatives are mentioned, and there is no direct comparison to tools like detect_broken_rotor_bars or compute_spectrum.

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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