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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

list_html_reports

List available HTML reports with metadata like file name, type, signal, and size to identify diagnostic reports without consuming tokens.

Instructions

    List all available HTML reports in reports/ directory.

    Returns list of reports with metadata (file name, type, signal, size).
    Does NOT return HTML content - only metadata to avoid token consumption.

    Returns:
        List of dicts with report information

    Example:
        >>> reports = list_html_reports()
        >>> print(f"Found {len(reports)} reports")
        >>> for r in reports:
        ...     print(f"- {r['file_name']}: {r['report_type']} for {r['signal_file']}")
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description handles transparency well by explicitly stating that the tool does NOT return HTML content and returns only metadata to avoid token consumption. It also describes the return format (list of dicts with report information).

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 with a brief main description and a useful example. It front-loads the action. The example adds clarity but could be slightly trimmed.

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?

Given zero parameters, existence of an output schema, and sibling tools, the description is fully complete. It covers what the tool does, what it returns, and a usage example.

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?

Since there are no parameters (schema coverage 100%), the baseline is 4. The description adds meaning by explaining the tool's purpose and return behavior beyond the empty schema.

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 uses specific language ('List all available HTML reports') and clearly distinguishes from sibling tools like report generators and other listers (e.g., list_signals, list_machine_manuals) by focusing on HTML reports and metadata-only return.

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 states what the tool does but does not provide explicit when-to-use or when-not-to-use guidance or mention alternatives like list_signals. Usage is implied rather than clarified.

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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