Skip to main content
Glama
LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

plot_signal

Generate interactive time-domain plots from CSV signal files to inspect quality, identify anomalies, and visualize transients for predictive maintenance.

Instructions

    Generate interactive time-domain signal plot.

    Creates an interactive HTML plot showing the signal in the time domain.
    Useful for inspecting signal quality, identifying anomalies, and visualizing transients.

    Args:
        signal_file: Name of the CSV file in data/signals/
        sampling_rate: Sampling frequency in Hz (default: 10000)
        time_range: [start_time, end_time] in seconds to zoom on a portion (optional)
        show_statistics: Show RMS, peak levels as horizontal lines (default: True)
        title: Custom plot title (optional)
        ctx: MCP context for progress/logging

    Returns:
        Path to generated HTML file

    Example:
        plot_signal(
            "bearing_signal.csv",
            sampling_rate=10000,
            time_range=[0.1, 0.3],  # Zoom on 100-300 ms
            show_statistics=True
        )
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_fileYes
sampling_rateNo
time_rangeNo
show_statisticsNo
titleNo

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 carries the full burden. It discloses that the output is an HTML file path and mentions zoom via time_range, but does not cover error handling (e.g., missing file) or any side effects. This is adequate but leaves some behavioral gaps.

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 structured as a docstring with sections (description, args, returns, example) and is concise at around 150 words. Every sentence adds value, with no redundancy or fluff.

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 and the presence of an output schema, the description covers key aspects: input parameters, output (HTML file path), and usage example. It could mention file overwriting or directory details, but overall it is sufficient for proper invocation.

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?

Schema description coverage is 0%, and the description adds explicit meaning for all 5 parameters: signal_file location, sampling_rate default, time_range as optional zoom array, show_statistics boolean, and title. The example further clarifies usage, more than compensating for the missing schema 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 'Generate interactive time-domain signal plot' and 'Creates an interactive HTML plot showing the signal in the time domain.' This distinguishes it from sibling tools like plot_spectrum and plot_envelope by specifying the time-domain focus, making the purpose specific and unambiguous.

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 mentions 'useful for inspecting signal quality, identifying anomalies, and visualizing transients,' which provides context for when to use. However, it does not explicitly state when not to use or name alternative tools, though the sibling list provides implicit guidance.

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