Skip to main content
Glama
LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

generate_test_signal

Generate synthetic vibration signals (e.g., bearing faults, imbalance) to validate predictive maintenance analyses when real data is unavailable.

Instructions

    Generate a test signal to validate analyses.

    Useful for testing algorithms without having real data available.

    Args:
        signal_type: Signal type ("bearing_fault", "gear_fault", "imbalance", "normal")
        duration: Signal duration in seconds (default: 10.0, gives 0.1 Hz frequency resolution)
        sampling_rate: Sampling frequency in Hz (default: 10000)
        noise_level: Noise level to add (default: 0.1)
        ctx: Context for logging

    Returns:
        Generated file name
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_typeNobearing_fault
durationNo
sampling_rateNo
noise_levelNo
random_seedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It mentions that a 10-second duration gives 0.1 Hz frequency resolution, adding behavioral context. However, it does not disclose side effects such as file storage location, overwriting behavior, or resource consumption, leaving gaps.

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 well-structured with a clear purpose sentence, followed by a bulleted Args section and a Returns line. It is reasonably concise, though the Args and Returns formatting could be slightly condensed without losing meaning.

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 the 5 parameters and no annotations, the description covers most but not all (missing random_seed). It includes a helpful note on frequency resolution. An output schema exists, so return details are handled elsewhere. Overall adequate but with a gap.

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?

With 0% schema description coverage, the description adds meaning for 4 out of 5 parameters (signal_type with example values, duration with frequency resolution note, sampling_rate, noise_level). However, the parameter 'random_seed' is missing from the description, so not all parameters are covered, slightly reducing the score from the baseline of 3.

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 'Generate a test signal to validate analyses' and 'Useful for testing algorithms without having real data available', specifying the verb and resource. It is distinct from sibling tools, which are analysis and plotting tools.

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

Usage Guidelines4/5

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

The description provides context for when to use it ('for testing algorithms without real data'), but does not explicitly exclude cases or name alternatives. However, the sibling tools are all analysis, so usage is implicitly clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/LGDiMaggio/predictive-maintenance-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server