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LGDiMaggio

Predictive Maintenance MCP Server

by LGDiMaggio

search_bearing_catalog

Retrieve bearing geometry from a verified catalog when a manual names a bearing but lacks dimensions. If not found, it signals a miss so you can request the geometry from the user.

Instructions

Search for bearing specifications in the local verified catalog.

Fallback for when the machine manual names a bearing but not its
geometry. The catalog is small BY DESIGN: only entries whose
geometry is traceable to a public source (mandatory `source`
citation). A miss is a legitimate negative outcome — ask the user
for the geometry; never guess it.

Args:
    bearing_id: Bearing designation (e.g. "6205", "SKF 6205-2RS")
    ctx: MCP context. Unused — see this module's docstring on logging.

Returns:
    Dictionary with bearing specifications if found, or a
    BearingCatalogMiss (status='not_found', suggestion,
    catalog_contains) when the bearing is not in the catalog.

Raises:
    Exception: If the catalog itself cannot be read (missing or
        malformed common_bearings_catalog.json).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bearing_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description carries full behavioral disclosure. It reveals the catalog is small by design, mandates source citation, clarifies that a miss is a valid result, describes the return structure (specifications or BearingCatalogMiss), and documents an exception when the catalog file cannot be read. This is comprehensive.

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-organized with clear Args, Returns, and Raises sections, and it front-loads the purpose. It is slightly verbose in referencing an internal docstring for logging, but the length is justified by the useful behavioral details.

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?

The tool is simple (one parameter) but the description covers input semantics, output behavior, error conditions, and catalog policy. Even with an output schema, the Returns section adds value by describing the miss object's fields. Complete given the tool's scope.

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?

The schema only provides a parameter name, type, and required flag with 0% description coverage. The description compensates fully by defining 'bearing_id' as a bearing designation and providing concrete examples ('6205', 'SKF 6205-2RS'), which is essential for correct invocation.

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 the tool searches for bearing specifications in a local verified catalog, using a specific verb and resource. It also distinguishes itself from sibling tools by noting it is a fallback when the machine manual names a bearing but lacks geometry, positioning it uniquely among the listed tools.

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?

The description explicitly names the scenario for use ('Fallback for when the machine manual names a bearing but not its geometry') and provides decision guidance for negative outcomes ('A miss is a legitimate negative outcome — ask the user for the geometry; never guess it'). This goes beyond general context to actionable instructions.

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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