Skip to main content
Glama
LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

search_bearing_catalog

Search local bearing catalogs for bearing specifications and geometry needed to calculate characteristic frequencies.

Instructions

    Search for bearing specifications in local bearing catalogs.

    This is a FALLBACK tool. LLM should use this ONLY when:
    1. Bearing designation found in machine manual
    2. Bearing geometry NOT found in machine manual
    3. Need geometry to calculate characteristic frequencies

    **IMPORTANT - LLM Usage Guidelines:**
    - Use this tool ONLY after checking machine manual first
    - DO NOT use this as primary source - manual takes precedence
    - If bearing not found here, ask user for specifications
    - DO NOT guess or estimate if bearing not in catalog
    - This catalog contains ~20 common ISO bearings (6200-6210, 6300-6310 series)
    - For uncommon bearings, tell user: "Bearing {X} not in catalog. Please provide geometry or upload manufacturer catalog to bearing_catalogs/"

    Search order:
    1. JSON catalog (common_bearings_catalog.json) - 20 common bearings
    2. In-memory fallback (legacy 6205, 6206)
    3. Returns None if not found

    Args:
        bearing_designation: Bearing designation (e.g., "6205", "SKF 6205-2RS", "FAG 6206")
        ctx: MCP context

    Returns:
        Dictionary with bearing specifications if found, None otherwise

    Example:
        >>> specs = search_bearing_catalog("SKF 6205-2RS")
        >>> print(f"Balls: {specs['num_balls']}, Diameter: {specs['ball_diameter_mm']} mm")
        Balls: 9, Diameter: 7.94 mm
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bearing_designationYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations present, but description fully discloses behavioral traits: fallback nature, search order (JSON then in-memory), catalog coverage (~20 bearings), and error handling instructions for LLM. No contradictions.

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?

Description is verbose but well-structured with clear sections, bullet points, and front-loaded purpose. Every sentence adds value, though could be slightly more concise.

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?

Covers all aspects: fallback logic, catalog scope, search order, error behavior, example output. Output schema exists, so return value explanation is optional but provided. Complete for a tool of this complexity.

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?

Schema has 0% coverage, but description provides significant added meaning: explains bearing designation can include prefixes like 'SKF', gives example usage with output. Compensates well for lack of 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?

Description clearly states it searches for bearing specifications in local bearing catalogs, with a specific verb and resource. It distinguishes itself from siblings by being a fallback tool, explicitly contrasting with manual lookup.

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

Usage Guidelines5/5

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

Provides explicit when-to-use (after manual, need geometry), when-not-to-use (primary source, guessing), and alternatives (ask user, upload catalog). Also details search order and fallback behavior.

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