Brandfetch MCP Server

Brandfetch MCP Server

The Brandfetch MCP server bridges large language models with the Brandfetch API, enabling AI assistants to perform brand discovery and retrieval of rich visual identity data. By exposing Brandfetch’s search and detail endpoints through the Model Context Protocol, developers can let LLMs query real‑world brand information without leaving their conversational environment.

This server solves a common pain point for AI‑powered design tools, marketing analytics platforms, and chatbot assistants: accessing up‑to‑date brand assets (logos, color palettes, typography) in a structured format. Instead of hard‑coding brand data or scraping websites, the MCP server authenticates with Brandfetch’s API and returns JSON payloads that can be directly consumed by downstream LLM pipelines. The integration eliminates the need for custom wrappers or manual API handling, allowing developers to focus on higher‑level logic.

Key capabilities include:

• Brand Search: Locate brands by name, receiving a list of matching entities with basic metadata.
• Detailed Brand Retrieval: Fetch comprehensive brand profiles—logos, colors, fonts, company details—by providing a domain or identifier.
• Field Filtering: Specify exact fields to return, reducing payload size and speeding up inference for LLMs.
• Interactive Prompts: Built‑in prompt templates guide users on how to construct search queries, ensuring correct parameter usage.
• Type‑safe, async implementation: The server is written in modern Python with full type annotations and asynchronous HTTP calls, promoting reliability and scalability.
• Robust error handling: Detailed logging and graceful failure paths help maintain stable AI workflows even when external services hiccup.

In practice, a marketing assistant could ask the LLM “Show me Nike’s color palette and logo,” which would trigger the tool via MCP, returning only the requested fields. A design system generator could automatically pull brand assets for a list of clients, while a chatbot could answer “What’s the official font of Apple?” without any additional coding. The MCP abstraction ensures that these calls remain consistent across different LLMs and platforms, making it a valuable addition to any AI‑centric product that relies on accurate brand representation.