# How to integrate Rev ai MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Rev ai MCP with Vercel AI SDK v6", "toolkit": "Rev ai", "toolkit_slug": "rev_ai", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/rev_ai/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/rev_ai/framework/ai-sdk.md", "updated_at": "2026-05-12T10:24:09.832Z" } ``` ## Introduction This guide walks you through connecting Rev ai to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Rev ai agent that can transcribe this meeting audio to text, get captions for latest video job, list all transcription jobs from last week through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Rev ai account through Composio's Rev ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Rev ai with - [OpenAI Agents SDK](https://composio.dev/toolkits/rev_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/rev_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/rev_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/rev_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/rev_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/rev_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/rev_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/rev_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/rev_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/rev_ai/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/rev_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/rev_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/rev_ai/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Rev ai integration - Using Composio's Tool Router to dynamically load and access Rev ai 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 Rev ai MCP server, and what's possible with it? The Rev ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Rev ai account. It provides structured and secure access to your speech-to-text workflows, so your agent can perform actions like transcribing audio, retrieving transcripts, managing custom vocabularies, and handling transcription jobs on your behalf. - Automated audio and video transcription: Effortlessly submit audio or video files for transcription, and let your agent monitor job progress until transcripts are ready. - Transcript and caption retrieval: Retrieve full transcripts in multiple formats or download caption files (SRT, VTT) for completed jobs—perfect for both review and publishing. - Custom vocabulary management: Enhance transcription accuracy by submitting and maintaining domain-specific vocabularies, including adding new terms and deleting unused sets. - Transcription job management: List, fetch, or delete transcription jobs by ID, making it easy for your agent to keep your workspace organized and up-to-date. - Real-time streaming transcription: Start live stream transcriptions for instant speech-to-text conversion, allowing your agent to process and react to spoken content in real time. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `REV_AI_DELETE_CUSTOM_VOCABULARY` | Delete Custom Vocabulary | Tool to delete a completed custom vocabulary and its data. Use when you need to remove an unused vocabulary after confirming it's no longer needed. | | `REV_AI_DELETE_JOB_BY_ID` | Delete Job By ID | Tool to delete a completed transcription job and its data. Use when you need to permanently remove a finished job after confirming it's no longer needed. | | `REV_AI_GET_ACCOUNT` | Get Account | Tool to retrieve developer account details. Use after authenticating with Rev AI. | | `REV_AI_GET_CAPTIONS` | Get Captions | Tool to retrieve captions (SRT or VTT) for a completed Rev.ai transcription job. Use after confirming the job status is 'completed'. | | `REV_AI_GET_CUSTOM_VOCABULARY_DETAILS` | Get Custom Vocabulary Details | Tool to retrieve custom vocabulary processing details. Use when needing to fetch the status and submitted phrases for a specific custom vocabulary after creation. | | `REV_AI_GET_JOB_BY_ID` | Get Job by ID | Tool to fetch details of a transcription job by its ID. Use when confirming job status and metadata are accurate. | | `REV_AI_GET_LIST_OF_JOBS` | Get List of Jobs | Tool to get list of transcription jobs from the past 30 days. Use when you need to retrieve and paginate through recent transcription tasks. | | `REV_AI_GET_TRANSCRIPT_BY_ID` | Get Transcript by ID | Tool to retrieve the transcript of a completed Rev.ai job. Use after confirming job is complete. Supports JSON format (with timestamps and speaker info) or plain text format. | | `REV_AI_START_STREAM_TRANSCRIPTION` | Start Stream Transcription | Tool to start a WebSocket transcription stream. Use when you need real-time speech-to-text streaming via Rev.ai. | | `REV_AI_SUBMIT_CUSTOM_VOCABULARY` | Submit Custom Vocabulary | Tool to submit a custom vocabulary for improved speech recognition. Use when you want to process domain-specific terms asynchronously. | | `REV_AI_SUBMIT_TRANSCRIPTION_JOB` | Submit Transcription Job | Tool to submit a new transcription job. Use when you have a media URL or file bytes ready for async processing. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Rev ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Rev ai. It provides structured and secure access so your agent can perform Rev ai 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 Rev ai 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 Rev ai-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: ["rev_ai"], }); 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 Rev ai 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 rev_ai, 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 Rev ai 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: ["rev_ai"], }); 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 rev_ai, 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 Rev ai 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 Rev ai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/rev_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/rev_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/rev_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/rev_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/rev_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/rev_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/rev_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/rev_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/rev_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/rev_ai/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/rev_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/rev_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/rev_ai/framework/crew-ai) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Rev ai MCP? With a standalone Rev ai MCP server, the agents and LLMs can only access a fixed set of Rev ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Rev ai 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 Rev ai tools. ### Can I manage the permissions and scopes for Rev ai while using Tool Router? Yes, absolutely. You can configure which Rev ai 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 Rev ai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)