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Auto Parts Inventory Agent

Auto Parts Inventory Agent

by ferflorwers25
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

buscar_repuestos

Read-onlyIdempotent

Find auto parts by text, vehicle make, model, and year, returning price in córdobas, stock per branch, and compatible models. Use it to check which parts fit a specific vehicle or machine.

Instructions

Busca repuestos y devuelve precio en córdobas, stock por sucursal y modelos compatibles.

Usala para preguntas como "¿qué pastillas de freno hay para una Hilux 2019?" o
"¿hay filtro de aire para una retroexcavadora 310L?". Combiná texto (qué repuesto)
con marca_vehiculo, modelo y anio (para qué vehículo). Se ignoran tildes y mayúsculas.

Input Schema

NameRequiredDescriptionDefault
anioNoAño del vehículo. Ej.: 2019.
textoNoPalabras a buscar en nombre, código, marca del repuesto o categoría. Todas deben aparecer, en cualquier orden. Ej.: 'pastillas delanteras', 'filtro aire', 'Bosch'.
limiteNoMáximo de repuestos a devolver (1 a 50).
modeloNoModelo del vehículo o máquina. Ej.: 'Hilux', 'Corolla', 'FZ 150', '310L'.
marca_vehiculoNoMarca del vehículo o máquina. Ej.: 'Toyota', 'Yamaha', 'John Deere'.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: the return contents (córdobas pricing, per-branch stock, compatible models) and the matching rule that accents and capitalization are ignored.

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?

Front-loads the purpose, then usage examples, then the parameter-combining rule, in three tight segments with no filler. Every sentence earns its place and the most important information leads.

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?

With 5 fully documented optional parameters, an output schema present (so return format needs no further explanation), and annotations covering the safety profile, the description supplies everything an agent needs to select and call it correctly.

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 coverage is 100%, so each parameter is documented at baseline. The description goes further by explaining how the filters combine — text (which part) AND marca_vehiculo/modelo/anio (which vehicle) — a semantic the schema does not state.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Busca repuestos") and even enumerates what it returns: price in córdobas, stock by branch, and compatible models. The purpose is unmistakable and clearly distinct from siblings like consulta_sql or stock_bajo_minimo, though no sibling is named explicitly.

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 concrete usage examples ("¿qué pastillas de freno hay para una Hilux 2019?") that map real questions to the tool, giving clear context for when to invoke it. It offers no explicit when-not-to-use guidance or named alternatives, so it stops short of the top band.

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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