Comprehensive API Documentation for Car Parts Inventory System: Categorization and Updates by Brand and Model Year

Managing your car parts inventory efficiently requires a robust API that allows precise categorization by brand and model year, along with seamless update capabilities. This documentation provides a complete reference on how to interact programmatically with our inventory system, focusing on categorizing car parts by brand and model year and updating inventory details like stock levels and pricing through secure API endpoints.

Table of Contents

1. Inventory System Overview
2. Authentication & Rate Limiting
3. API Endpoints
   • Brand Management
   • Model Year Management
   • Car Parts Management
4. Data Structures & Categorization Scheme
5. Use Case Examples
6. Error Handling Schema
7. Best Practices for Integration
8. Enhancing Inventory Management with Zigpoll Integration
9. Summary

Inventory System Overview

Our inventory system is architected to facilitate detailed tracking and management of car parts categorized hierarchically by:

• Brand (e.g., Toyota, Ford)
• Model Year (e.g., 2018, 2022)
• Part Category (e.g., brakes, engine)
• Individual Part

This categorization enables precise filtering and retrieval of parts compatible with specific car brand-model year combinations.

Core Features:

• Hierarchical categorization supporting brand and model year filtering
• Real-time update capabilities for stock, pricing, descriptions
• Bulk update endpoints for large-scale inventory management
• Pagination and filtering to optimize data retrieval performances
• Secure token-based authentication with rate limiting

Authentication & Rate Limiting

All requests require authentication via an API token included in the request header:

Authorization: Bearer YOUR_API_TOKEN

Obtain and manage your API tokens through our Developer Portal.

Rate Limits:

Exceeding limits results in HTTP 429 responses. Implement retry logic with exponential backoff to handle rate limit errors gracefully.

API Endpoints

Brand Management

Brands are the primary identifier for car parts.

• List All Brands
  GET /brands
  Retrieves all car brands available.

• Get Brand Details
  GET /brands/{brandId}
  Fetch detailed information on a specific brand.

• Create a New Brand
  POST /brands
  Request body:

  {
    "name": "Tesla",
    "description": "Electric vehicle manufacturer"
  }
  

  Creates a new brand entry.

Model Year Management

Model years are nested under brands to specify part compatibility.

• List Model Years for a Brand
  GET /brands/{brandId}/model-years
  Retrieves available model years for the brand.

• Add Model Year to a Brand
  POST /brands/{brandId}/model-years
  Request body:

  {
    "year": 2024
  }
  

  Adds a new model year for the brand.

• Delete a Model Year
  DELETE /brands/{brandId}/model-years/{modelYearId}
  Removes a model year from the brand.

Car Parts Management

Parts are assigned at the intersection of brand and model year, and grouped by categories.

• List Parts by Brand and Model Year
  GET /brands/{brandId}/model-years/{modelYearId}/parts
  Optional query parameters:

  • category (e.g., "engine", "brakes")
  • limit (default 50)
  • offset (pagination)

• Get Part Details
  GET /parts/{partId}

• Add New Part
  POST /brands/{brandId}/model-years/{modelYearId}/parts
  Request body example:

  {
    "name": "Oil Filter",
    "category": "engine",
    "sku": "OF-76543",
    "stock": 150,
    "price": 15.00,
    "description": "Oil filter for 2011 Toyota Camry"
  }
  

• Update Part Details or Stock
  PATCH /parts/{partId}
  Supports partial updates. For example:

  {
    "stock": 130,
    "price": 14.50,
    "description": "Updated oil filter for 2011 Toyota Camry"
  }
  

• Delete Part
  DELETE /parts/{partId}

• Bulk Upload Parts
  POST /parts/bulk-upload
  Accepts a JSON array of parts objects to efficiently add or update multiple parts.

Data Structures & Categorization Scheme

• Brand

  • id (integer)
  • name (string)
  • description (optional string)

• Model Year

  • id (integer)
  • brandId (integer)
  • year (integer)

• Part

  • id (integer)
  • brandId (integer)
  • modelYearId (integer)
  • name (string)
  • category (string) — predefined values include: ["brakes", "engine", "suspension", "electronics", "transmission"]
  • sku (string)
  • stock (integer)
  • price (float) — in USD
  • description (optional string)
  • createdAt (ISO-8601 timestamp)
  • updatedAt (ISO-8601 timestamp)

This normalized structure ensures fast, reliable queries by brand and year, improving inventory accuracy.

Use Case Examples

1. Fetch Suspension Parts for 2019 Ford Models

GET /brands/2/model-years/2019/parts?category=suspension&limit=20
Authorization: Bearer YOUR_API_TOKEN

2. Add Model Year 2023 to Toyota

POST /brands/1/model-years
Content-Type: application/json
Authorization: Bearer YOUR_API_TOKEN

{
  "year": 2023
}

3. Update Stock Quantity for a Part

PATCH /parts/101
Content-Type: application/json
Authorization: Bearer YOUR_API_TOKEN

{
  "stock": 90
}

4. Bulk Upload Multiple Parts

POST /parts/bulk-upload
Content-Type: application/json
Authorization: Bearer YOUR_API_TOKEN

[
  {
    "brandId": 1,
    "modelYearId": 3,
    "name": "Spark Plug",
    "category": "engine",
    "sku": "SP-9001",
    "stock": 50,
    "price": 10.00,
    "description": "Spark plug for 2012 Toyota Camry"
  },
  {
    "brandId": 1,
    "modelYearId": 3,
    "name": "Fuel Pump",
    "category": "engine",
    "sku": "FP-9910",
    "stock": 20,
    "price": 150.00
  }
]

Error Handling Schema

All errors return a JSON response with an error object containing code and message fields.

Example:

{
  "error": {
    "code": 404,
    "message": "Brand with ID 99 not found"
  }
}

Best Practices for Integration

• Always paginate large data responses using limit and offset parameters to improve performance.
• Cache brand and model year data locally as these update infrequently.
• Validate input payloads client-side to minimize 400 errors.
• Use bulk endpoints when updating or adding multiple parts simultaneously.
• Keep your API tokens secure and never expose them in public repositories or client-side code.
• Implement retry logic with exponential backoff when receiving 429 errors due to rate limiting.
• Monitor and automate stock level adjustments to prevent overselling and stockouts.

Enhancing Inventory Management with Zigpoll Integration

Integrate real-time customer and mechanic feedback through Zigpoll to optimize your car parts inventory:

• Deploy interactive polls to identify trending car parts by brand and model year.
• Collect shortage feedback to proactively update stock with PATCH /parts/{partId} calls.
• Analyze market preferences to guide smart inventory scaling by brand or model year.

Learn how to align your inventory updates with in-the-field demand using Zigpoll’s API-driven polling system. This synergy between polling data and inventory APIs can significantly reduce stock mismatches and boost customer satisfaction.

Explore more at https://zigpoll.com.

Summary

This API documentation delivers a detailed, SEO-optimized, and developer-centric guide to managing your car parts inventory, emphasizing exact categorization by brand and model year, and how to programmatically update inventory details.

Key Points:

• Use hierarchical endpoints for brands, model years, and parts
• Perform real-time stock and pricing updates via PATCH requests
• Utilize bulk upload endpoints to streamline large-scale updates
• Handle errors clearly and respect API rate limits
• Integrate user feedback via Zigpoll for data-driven stock management

Harness the power of this structured API to automate and scale your automotive parts inventory management effectively, ensuring customers get the right parts for the right vehicles every time