# How to integrate Pexels MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Pexels MCP with Vercel AI SDK v6", "toolkit": "Pexels", "toolkit_slug": "pexels", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/pexels/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/pexels/framework/ai-sdk.md", "updated_at": "2026-05-12T10:21:52.555Z" } ``` ## Introduction This guide walks you through connecting Pexels to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Pexels agent that can find free stock photos of beaches, get trending pexels videos this week, list featured photography collections through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Pexels account through Composio's Pexels MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Pexels with - [ChatGPT](https://composio.dev/toolkits/pexels/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/pexels/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/pexels/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/pexels/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/pexels/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/pexels/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/pexels/framework/codex) - [Cursor](https://composio.dev/toolkits/pexels/framework/cursor) - [VS Code](https://composio.dev/toolkits/pexels/framework/vscode) - [OpenCode](https://composio.dev/toolkits/pexels/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/pexels/framework/openclaw) - [Hermes](https://composio.dev/toolkits/pexels/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/pexels/framework/cli) - [Google ADK](https://composio.dev/toolkits/pexels/framework/google-adk) - [LangChain](https://composio.dev/toolkits/pexels/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/pexels/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/pexels/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/pexels/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Pexels integration - Using Composio's Tool Router to dynamically load and access Pexels 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 Pexels MCP server, and what's possible with it? The Pexels MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Pexels account. It provides structured and secure access to the Pexels media library, so your agent can search photos and videos, fetch curated collections, explore trending media, and retrieve detailed asset information on your behalf. - Photo and video search: Instantly find high-quality photos and videos from Pexels based on keywords, categories, or filters using natural language queries. - Curated and trending media discovery: Let your agent fetch the latest curated photos or surface popular videos to keep your content pipeline fresh and engaging. - Collection management and exploration: Access your own collections or featured Pexels collections, and pull all media from any collection with ease. - Detailed asset retrieval: Retrieve metadata, dimensions, and direct image or video URLs for any specific photo or video asset by ID. - Content integration for creative workflows: Seamlessly pull media assets into your applications, presentations, or creative projects with minimal effort. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PEXELS_COLLECTION_MEDIA` | Get Collection Media | Tool to get all media within a collection by its ID. Use when you need to fetch paginated media from a specific collection. | | `PEXELS_CURATED_PHOTOS` | Get Curated Photos | Tool to get real-time curated photos. Use when you need to fetch curated photos with pagination support. | | `PEXELS_FEATURED_COLLECTIONS` | Featured Collections | Tool to get featured collections. Use when you need curated collections of photos and videos with pagination support. | | `PEXELS_GET_PHOTO` | Get Photo | Tool to retrieve detailed information about a specific photo. Use when you have a valid photo ID to fetch metadata including dimensions, photographer details, and image URLs. Use after confirming the photo ID from search or curated endpoints. | | `PEXELS_GET_VIDEO_BY_ID` | Get Video by ID | Tool to retrieve detailed information about a specific video from Pexels. Use when you have a valid video ID to fetch metadata including dimensions, duration, videographer details, and available video file versions. | | `PEXELS_MY_COLLECTIONS` | Get My Collections | Tool to get all of the user's collections on Pexels. Use when you need to list a user's collections with pagination support. | | `PEXELS_POPULAR_VIDEOS` | Get Popular Videos | Tool to retrieve current popular Pexels videos. Use when you want to fetch trending videos from Pexels. | | `PEXELS_SEARCH_PHOTOS` | Search Photos | Tool to search for photos on Pexels. Use when you need to retrieve photos by a search term and optional filters. Call after confirming you have a valid Pexels API key. Response image URLs are nested under `photo.src.` (e.g., `photo.src.original`, `photo.src.landscape`, `photo.src.medium`); the top-level `url` field is not sufficient for accessing specific image sizes. | | `PEXELS_SEARCH_VIDEOS` | Search Videos | Tool to search for videos on Pexels by query and optional filters. Use when you need to find relevant video assets. Combining multiple filters with a narrow query may return few or no results; only apply strict filter combinations when explicitly required. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Pexels MCP server is an implementation of the Model Context Protocol that connects your AI agent to Pexels. It provides structured and secure access so your agent can perform Pexels 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 Pexels 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 Pexels-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: ["pexels"], }); 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 Pexels 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 pexels, 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 Pexels 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: ["pexels"], }); 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 pexels, 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 Pexels 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 Pexels MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/pexels/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/pexels/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/pexels/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/pexels/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/pexels/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/pexels/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/pexels/framework/codex) - [Cursor](https://composio.dev/toolkits/pexels/framework/cursor) - [VS Code](https://composio.dev/toolkits/pexels/framework/vscode) - [OpenCode](https://composio.dev/toolkits/pexels/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/pexels/framework/openclaw) - [Hermes](https://composio.dev/toolkits/pexels/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/pexels/framework/cli) - [Google ADK](https://composio.dev/toolkits/pexels/framework/google-adk) - [LangChain](https://composio.dev/toolkits/pexels/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/pexels/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/pexels/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/pexels/framework/crew-ai) ## Related Toolkits - [Figma](https://composio.dev/toolkits/figma) - Figma is a collaborative interface design tool for teams and individuals. It streamlines design workflows with real-time collaboration and easy sharing. - [Abyssale](https://composio.dev/toolkits/abyssale) - Abyssale is a creative automation platform for generating images, videos, GIFs, PDFs, and HTML5 content programmatically. It streamlines and scales visual content production for marketing, design, and operations teams. - [Alttext ai](https://composio.dev/toolkits/alttext_ai) - AltText.ai is a service that generates alt text for images automatically. It helps boost accessibility and SEO for your visual content. - [Bannerbear](https://composio.dev/toolkits/bannerbear) - Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates. - [Canva](https://composio.dev/toolkits/canva) - Canva is a drag-and-drop design suite for creating professional graphics, presentations, and marketing materials. It makes it easy for anyone to design with beautiful templates and a vast library of elements. - [Claid ai](https://composio.dev/toolkits/claid_ai) - Claid.ai delivers AI-driven image editing APIs for tasks like background removal, upscaling, and color correction. It helps automate and enhance image workflows with powerful, developer-friendly tools. - [Cloudinary](https://composio.dev/toolkits/cloudinary) - Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally. - [Cults](https://composio.dev/toolkits/cults) - Cults is a digital marketplace for 3D printing models, connecting designers and makers. It lets creators share, sell, and discover a huge variety of printable designs easily. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. - [Dreamstudio](https://composio.dev/toolkits/dreamstudio) - DreamStudio is Stability AI’s platform for generating and editing images with AI. It lets you easily turn ideas into stunning visuals, fast. - [Dynapictures](https://composio.dev/toolkits/dynapictures) - Dynapictures is a cloud-based platform for generating personalized images at scale. Instantly create hundreds of custom visuals using your data sources, like Google Sheets. - [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools. - [Gamma](https://composio.dev/toolkits/gamma) - Gamma is an AI-powered platform for making beautiful, interactive presentations and documents. It lets anyone create and share engaging decks with minimal effort. - [Html to image](https://composio.dev/toolkits/html_to_image) - Html to image converts HTML and CSS into images or captures web page screenshots. Instantly generate visuals from code or web content—no manual screenshots needed. - [Imagior](https://composio.dev/toolkits/imagior) - Imagior is an AI-powered image generation platform that lets you create and customize images using dynamic templates and APIs. Perfect for businesses and creators needing fast, scalable visuals without design hassle. - [Imejis io](https://composio.dev/toolkits/imejis_io) - Imejis io is an API-based image generation platform with powerful customization and template support. It lets you create and modify images in seconds, no manual design work required. - [Imgix](https://composio.dev/toolkits/imgix) - Imgix is a real-time image processing and delivery service for developers. It helps you optimize, transform, and deliver images efficiently at any scale. - [Kraken io](https://composio.dev/toolkits/kraken_io) - Kraken.io is an image optimization and compression platform. It helps you shrink image file sizes while keeping visual quality intact. - [Logo dev](https://composio.dev/toolkits/logo_dev) - Logo.dev is an API and database for high-resolution company logos and brand metadata. Instantly fetch official logos from any domain without scraping or manual searching. - [Miro](https://composio.dev/toolkits/miro) - Miro is a collaborative online whiteboard platform for teams to brainstorm, design, and manage projects visually. It streamlines teamwork by enabling real-time idea sharing, diagramming, and workflow planning in a single space. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Pexels MCP? With a standalone Pexels MCP server, the agents and LLMs can only access a fixed set of Pexels tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Pexels 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 Pexels tools. ### Can I manage the permissions and scopes for Pexels while using Tool Router? Yes, absolutely. You can configure which Pexels 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 Pexels data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)