# How to integrate Spotlightr MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Spotlightr MCP with Vercel AI SDK v6", "toolkit": "Spotlightr", "toolkit_slug": "spotlightr", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/spotlightr/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/spotlightr/framework/ai-sdk.md", "updated_at": "2026-05-12T10:26:57.888Z" } ``` ## Introduction This guide walks you through connecting Spotlightr to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Spotlightr agent that can show me your top 5 most viewed videos, get viewer engagement stats for video id 1234, list videos with highest watch time this week through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Spotlightr account through Composio's Spotlightr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Spotlightr with - [OpenAI Agents SDK](https://composio.dev/toolkits/spotlightr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/spotlightr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/spotlightr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/spotlightr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/spotlightr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/spotlightr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/spotlightr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/spotlightr/framework/cli) - [Google ADK](https://composio.dev/toolkits/spotlightr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/spotlightr/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/spotlightr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/spotlightr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/spotlightr/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Spotlightr integration - Using Composio's Tool Router to dynamically load and access Spotlightr 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 Spotlightr MCP server, and what's possible with it? The Spotlightr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Spotlightr account. It provides structured and secure access to your video content and analytics, so your agent can perform actions like retrieving top-performing videos, accessing detailed video metrics, and surfacing engagement insights automatically on your behalf. - Top video discovery and listing: Instantly ask your agent to fetch and list your most viewed or highest performing Spotlightr videos. - Video analytics and metrics retrieval: Have your agent pull comprehensive analytics for a specific video, including views, engagement rates, unique viewers, and total watch time. - Engagement insight extraction: Let the agent surface actionable insights about viewer engagement for any video, making it easy to spot trends and opportunities. - Automated reporting support: Your agent can collect and summarize video performance data, making regular reporting and decision-making faster and more data-driven. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `SPOTLIGHTR_ADD_DOMAIN` | Add Domain | Tool to add a whitelisted domain for embedding Spotlightr videos. Use when you need to whitelist a new domain for video embedding. | | `SPOTLIGHTR_CREATE_GROUP` | Create Group | Tool to create a new project (group) in Spotlightr. Use when you need to organize videos into a new project or group. | | `SPOTLIGHTR_CREATE_VIDEO` | Create Video | Tool to create a video in Spotlightr by uploading a file or linking from an external source. Use when you need to add a new video from YouTube, Google Drive, Vimeo, or other sources. | | `SPOTLIGHTR_DELETE_VIDEO` | Delete Video | Tool to delete or remove videos from Spotlightr projects. Use when you need to permanently delete one or more videos by their IDs. | | `SPOTLIGHTR_GET_DOMAINS` | Get Domains | Tool to retrieve whitelisted domains for a Spotlightr account. Use when you need to list all domains approved for embedding videos. | | `SPOTLIGHTR_GET_TOP_VIDEOS` | Get Top Videos | Tool to retrieve the top videos from a Spotlightr account. Use when you need to list the most viewed videos. | | `SPOTLIGHTR_GET_VIDEO_METRICS` | Get Video Metrics | Tool to retrieve analytics metrics for a specified video. Use when you have a video ID and need its metrics (loads, plays, playRate, completionRate, shares, etc.). | | `SPOTLIGHTR_GET_VIDEO_SOURCE` | Get Video Source | Tool to get or replace the video source for an existing video in Spotlightr. Use when you need to update a video's source URL. | | `SPOTLIGHTR_GET_VIDEO_VIEWS` | Get Video Views | Tool to retrieve video view data with optional filtering by viewer ID and watch status. Use when you need detailed view records for a specific video. | | `SPOTLIGHTR_LIST_GROUPS` | List Groups | Tool to retrieve all projects (groups) in a Spotlightr account. Use when you need to list all available projects or groups. | | `SPOTLIGHTR_LIST_VIDEOS` | List Videos | Tool to retrieve videos from a Spotlightr account. Use when you need to list all videos or filter by specific video ID or project. | | `SPOTLIGHTR_SEARCH_GLOBAL` | Search Global | Tool to perform account-wide search across all videos and content in Spotlightr. Use when you need to find specific videos, projects, or content by name or keyword. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Spotlightr MCP server is an implementation of the Model Context Protocol that connects your AI agent to Spotlightr. It provides structured and secure access so your agent can perform Spotlightr 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 Spotlightr 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 Spotlightr-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: ["spotlightr"], }); 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 Spotlightr 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 spotlightr, 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 Spotlightr 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: ["spotlightr"], }); 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 spotlightr, 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 Spotlightr 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 Spotlightr MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/spotlightr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/spotlightr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/spotlightr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/spotlightr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/spotlightr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/spotlightr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/spotlightr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/spotlightr/framework/cli) - [Google ADK](https://composio.dev/toolkits/spotlightr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/spotlightr/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/spotlightr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/spotlightr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/spotlightr/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. - [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Spotlightr MCP? With a standalone Spotlightr MCP server, the agents and LLMs can only access a fixed set of Spotlightr tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Spotlightr 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 Spotlightr tools. ### Can I manage the permissions and scopes for Spotlightr while using Tool Router? Yes, absolutely. You can configure which Spotlightr 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 Spotlightr data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)