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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

read_manual_excerpt

Read text from machine manuals to answer queries about parameters, maintenance, and procedures. Use extracted content exclusively for LLM context.

Instructions

    Read text excerpt from machine manual (PDF or TXT).

    Useful for providing context to LLM for questions about
    specific machine parameters, maintenance procedures, etc.

    **Token Warning**: Reading many pages can consume significant tokens.
    Start with max_pages=10 and increase if needed.

    **IMPORTANT - LLM Usage Guidelines:**
    - This tool returns ONLY the text extracted from the manual
    - Base your answers EXCLUSIVELY on the returned text
    - DO NOT add information not present in the extracted text
    - If information is not found in the text, clearly state "Not found in manual"
    - DO NOT make assumptions or fill gaps with general knowledge
    - If user needs more pages, suggest increasing max_pages parameter
    - ALWAYS cite the manual when answering: "According to the manual..."

    Args:
        manual_filename: Manual filename in resources/machine_manuals/ (PDF or TXT)
        max_pages: Maximum number of pages to extract (default: 10, ignored for TXT files)
        ctx: MCP context

    Returns:
        Extracted text from manual

    Example:
        >>> text = read_manual_excerpt("pump_manual.pdf", max_pages=5)
        >>> # LLM can now answer: "What bearings are recommended for this pump?"
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
manual_filenameYes
max_pagesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses token consumption, that answers must be based solely on extracted text, and that max_pages is ignored for TXT files. No contradictions with 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections and bold headers; front-loaded with purpose. Slightly verbose but every sentence serves a purpose, including the important LLM usage guidelines.

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

Completeness5/5

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

Complete coverage: purpose, parameters, usage, LLM guidelines, and example. Contextually sufficient given the tool's simplicity and presence of output schema.

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?

Adds meaning beyond schema by specifying manual_filename location and format, default and behavior of max_pages, and an example usage. Schema coverage is 0%, so this compensation is adequate.

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 it reads text excerpts from machine manuals (PDF or TXT), distinguishing it from sibling tools focused on signal analysis and reports.

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?

Provides explicit context for LLM use, token warning, and iterative suggestion to start with max_pages=10. Does not explicitly exclude alternative tools but is clear about when to use.

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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