# How to integrate Brandfetch MCP with Pydantic AI ```json { "title": "How to integrate Brandfetch MCP with Pydantic AI", "toolkit": "Brandfetch", "toolkit_slug": "brandfetch", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/brandfetch/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/brandfetch/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:03:59.051Z" } ``` ## Introduction This guide walks you through connecting Brandfetch to Pydantic AI using the Composio tool router. By the end, you'll have a working Brandfetch agent that can get the official logo for apple inc, list brand colors used by starbucks, find company info for nike by domain through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Brandfetch account through Composio's Brandfetch MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Brandfetch with - [OpenAI Agents SDK](https://composio.dev/toolkits/brandfetch/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/brandfetch/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/brandfetch/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/brandfetch/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/brandfetch/framework/codex) - [OpenClaw](https://composio.dev/toolkits/brandfetch/framework/openclaw) - [Hermes](https://composio.dev/toolkits/brandfetch/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/brandfetch/framework/cli) - [Google ADK](https://composio.dev/toolkits/brandfetch/framework/google-adk) - [LangChain](https://composio.dev/toolkits/brandfetch/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/brandfetch/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/brandfetch/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/brandfetch/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/brandfetch/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Brandfetch - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Brandfetch workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## What is the Brandfetch MCP server, and what's possible with it? The Brandfetch MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Brandfetch account. It provides structured and secure access to company logos, brand colors, and comprehensive brand assets, so your agent can perform actions like fetching brand information, identifying merchants, retrieving brand logos, and searching brands on your behalf. - Fetch complete brand profiles: Instantly retrieve logos, color palettes, fonts, and visual identity details for any brand using domain, brand ID, ISIN, or ticker symbol. - Get company firmographic data: Let your agent pull in-depth company information, including industry and organization details, for any brand identifier. - Merchant identification from transactions: Seamlessly map credit card transaction labels or raw payment descriptions to merchant brands and enrich transaction data with brand assets. - Retrieve and customize brand logos: Fetch high-quality and up-to-date brand logos, icons, or symbols in light or dark themes and in various dimensions. - Search and match brands by name: Enable your agent to autocomplete and match brand names to their official URLs and icons, perfect for enriching user experiences or directories. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `BRANDFETCH_GET_BRAND_INFO` | Get Brand Information | Retrieves brand information including logos, colors, fonts, and company details using a domain, Brand ID, ISIN, or stock ticker. Logo data may be absent for some domains — do not assume logos are always returned. The response includes multiple logo types (e.g., icon, logo) and themes; explicitly select the desired type and size rather than defaulting to the first URL. | | `BRANDFETCH_GET_GRAPHQL_VERSION` | Get GraphQL API Version | Tool to retrieve the Brandfetch GraphQL API version. Use when you need to check the current API version via the GraphQL endpoint. | | `BRANDFETCH_GET_TAXONOMY` | Get Brandfetch Taxonomy | Tool to retrieve Brandfetch's taxonomy via GraphQL API. Use this to get a complete list of industries, countries, and geographic regions used in Brandfetch's classification system. The taxonomy includes hierarchical industry data with parent-child relationships. | | `BRANDFETCH_GET_TRANSACTION_INFO` | Get Transaction Info | This tool converts payment transaction labels into detailed merchant brand information. It takes a transaction label (like what you see on your credit card statement) and returns comprehensive brand data (including logos, colors, fonts, and company information). It is useful for identifying merchants and enriching transaction data with detailed brand information. | | `BRANDFETCH_LIST_SUBSCRIBABLE_EVENTS` | List Subscribable Events | Tool to retrieve all available webhook event types that can be subscribed to via the Brandfetch GraphQL API. Returns event names and descriptions for webhook configuration. Available events include brand.claimed, brand.deleted, brand.updated, brand.company.updated, and brand.verified. | | `BRANDFETCH_LIST_WEBHOOKS` | List Webhooks | Tool to retrieve a list of all webhooks via GraphQL API. Use when you need to query webhook configurations and their statuses in the Brandfetch system. | | `BRANDFETCH_SEARCH_BRANDS` | Search Brands | Searches for brands by name and returns matching brand information including URLs and icons, enabling rich autocomplete experiences. Use this tool first to resolve a vague name or ticker to a precise domain or brandId before calling BRANDFETCH_GET_BRAND_INFO or BRANDFETCH_GET_LOGO. Results may include multiple candidates; disambiguate using the domain, geography, qualityScore, and verified fields rather than defaulting to the first result. Returns empty results for new or niche brands. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Brandfetch MCP server is an implementation of the Model Context Protocol that connects your AI agent to Brandfetch. It provides structured and secure access so your agent can perform Brandfetch operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Brandfetch - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Brandfetch - MCPServerStreamableHTTP connects to the Brandfetch MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Brandfetch tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Brandfetch composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["brandfetch"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Brandfetch endpoint - The agent uses GPT-5 to interpret user commands and perform Brandfetch operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent brandfetch_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[brandfetch_mcp], instructions=( "You are a Brandfetch assistant. Use Brandfetch tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Brandfetch API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Brandfetch.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Brandfetch composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["brandfetch"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent brandfetch_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[brandfetch_mcp], instructions=( "You are a Brandfetch assistant. Use Brandfetch tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Brandfetch.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Brandfetch through Composio's Tool Router. With this setup, your agent can perform real Brandfetch actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Brandfetch for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Brandfetch MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/brandfetch/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/brandfetch/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/brandfetch/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/brandfetch/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/brandfetch/framework/codex) - [OpenClaw](https://composio.dev/toolkits/brandfetch/framework/openclaw) - [Hermes](https://composio.dev/toolkits/brandfetch/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/brandfetch/framework/cli) - [Google ADK](https://composio.dev/toolkits/brandfetch/framework/google-adk) - [LangChain](https://composio.dev/toolkits/brandfetch/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/brandfetch/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/brandfetch/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/brandfetch/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/brandfetch/framework/crew-ai) ## Related Toolkits - [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing. - [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages. - [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections. - [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach. - [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease. - [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic. - [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns. - [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active. - [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place. - [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES. - [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools. - [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools. - [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes. - [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management. - [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals. - [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams. - [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience. - [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs. - [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics. - [Doppler marketing automation](https://composio.dev/toolkits/doppler_marketing_automation) - Doppler marketing automation is a platform for creating, sending, and tracking email campaigns. It helps you automate marketing workflows and manage subscriber lists for better engagement. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Brandfetch MCP? With a standalone Brandfetch MCP server, the agents and LLMs can only access a fixed set of Brandfetch tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Brandfetch and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Pydantic AI? Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Brandfetch tools. ### Can I manage the permissions and scopes for Brandfetch while using Tool Router? Yes, absolutely. You can configure which Brandfetch scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Brandfetch data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)