Brandfetch MCP

🚀 Brandfetch MCP Server

The Model Context Protocol (MCP) server for the Brandfetch API, serving as a bridge between large language model (LLM) applications and the Brandfetch API, enabling AI assistants to search for brands and retrieve comprehensive brand information.

🚀 Quick Start

This MCP server acts as a bridge between large language model (LLM) applications and the Brandfetch API, enabling AI assistants to search for brands and retrieve comprehensive brand information. By implementing the Model Context Protocol, this server seamlessly integrates Brandfetch's brand data capabilities into LLM-driven applications.

✨ Features

• Brand Search: Search for brands by name and obtain basic information.
• Detailed Brand Information: Retrieve comprehensive brand data, including logos, colors, fonts, and company details.
• Field Filtering: Request specific information to optimize response size and processing.
• Interactive Prompts: Built - in prompts guide users to use the API correctly.
• Type - Safe Implementation: A fully typed Python codebase supporting modern asynchronous operations.
• Robust Error Handling: Comprehensive error handling and logging.

📦 Installation

Prerequisites

• Python 3.9 or higher
• Brandfetch API credentials (API key and client ID)

Using uv (Recommended)

# Create and navigate to a new project directory
uv init brandfetch-mcp
cd brandfetch-mcp

# Clone this repository
git clone https://github.com/VincentSolconBraze/brandfetch-mcp.git .

# Add dependencies
uv add "mcp[cli]" httpx python-dotenv

# For development
uv add --dev pytest pytest-asyncio pytest-cov ruff pyright pre-commit

Using pip

# Clone this repository
git clone https://github.com/VincentSolconBraze/brandfetch-mcp.git
cd brandfetch-mcp

# Install dependencies
pip install "mcp[cli]" httpx python-dotenv

# For development
pip install pytest pytest-asyncio pytest-cov ruff pyright pre-commit

📚 Documentation

Configuration

1. Create a .env file based on the example:

cp .env.example .env

2. Add your Brandfetch API credentials to the .env file:

BRANDFETCH_API_KEY=your_api_key
BRANDFETCH_CLIENT_ID=your_client_id

You can create an account and obtain credentials on Brandfetch.

Usage

Run via Claude Desktop

# Run in Claude Desktop
claudius run

Run via MCP Inspector

# Run in MCP Inspector
inspect

Direct Execution

# Run directly in the terminal
python3 main.py

Available Tools

• search_brands: Search for brands by name and obtain basic information.
• get_brand_info: Retrieve detailed information about a specified brand.

Parameter Explanation

• search_brands:
  • query: The keyword for the brand to search.
  • limit: The limit on the number of results to return.
• get_brand_info:
  • brand_id: The unique identifier of the brand.
  • fields: Specific fields to retrieve (optional).

💻 Usage Examples

Basic Usage

# Search for brands
search_brands(query="apple", limit=5)

# Get specific brand information
get_brand_info(brand_id="12345")

Advanced Usage

# Combine with field filtering
get_brand_info(brand_id="67890", fields=["name", "logo"])

🔧 Technical Details

Testing

Run the test suite

# Run all tests
pytest tests/

Generate a coverage report

# Generate a coverage report
coverage run -m pytest tests/ && coverage html

Documentation

• Documentation: Project Documentation
• API Reference: API Documentation

Contribution

• Contribution Guide: How to Contribute
• Code Style: Code Guidelines

Security

• Security Report: If you find any security issues, please contact [security@brandfetch.com]

📄 License

This project is licensed under the MIT License.
MIT License Content