# How to integrate Brandfetch MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Brandfetch MCP with Vercel AI SDK v6", "toolkit": "Brandfetch", "toolkit_slug": "brandfetch", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/brandfetch/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/brandfetch/framework/ai-sdk.md", "updated_at": "2026-05-12T10:03:59.051Z" } ``` ## Introduction This guide walks you through connecting Brandfetch to Vercel AI SDK v6 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 Vercel AI SDK 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) - [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 and configure a Vercel AI SDK agent with Brandfetch integration - Using Composio's Tool Router to dynamically load and access Brandfetch tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## 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 you begin, make sure you have: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 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 required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); ``` ### 5. Create Tool Router session and initialize MCP client 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 mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all Brandfetch-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["brandfetch"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available Brandfetch tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to brandfetch, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use Brandfetch tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["brandfetch"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to brandfetch, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a Brandfetch agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## 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) - [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 Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 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)