# How to integrate Ai ml api MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Ai ml api MCP with Vercel AI SDK v6", "toolkit": "Ai ml api", "toolkit_slug": "ai_ml_api", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/ai_ml_api/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/ai_ml_api/framework/ai-sdk.md", "updated_at": "2026-05-12T10:00:39.089Z" } ``` ## Introduction This guide walks you through connecting Ai ml api to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Ai ml api agent that can check if this image contains unsafe content, summarize this customer chat conversation, generate a polite reply to this message through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Ai ml api account through Composio's Ai ml api MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Ai ml api with - [OpenAI Agents SDK](https://composio.dev/toolkits/ai_ml_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ai_ml_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ai_ml_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ai_ml_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ai_ml_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ai_ml_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ai_ml_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ai_ml_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/ai_ml_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ai_ml_api/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/ai_ml_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/ai_ml_api/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/ai_ml_api/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Ai ml api integration - Using Composio's Tool Router to dynamically load and access Ai ml api 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 Ai ml api MCP server, and what's possible with it? The Ai ml api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ai ml api account. It provides structured and secure access to powerful AI/ML models, so your agent can generate text, moderate user content, and automate intelligent workflows on your behalf. - Automated content moderation: Instantly classify and filter user-generated text or images using advanced moderation models to keep your platform safe and compliant. - Dynamic text generation: Have your agent generate chat responses, write creative copy, or complete conversations using state-of-the-art language models. - Context-aware conversation handling: Let your agent analyze conversation history and produce coherent, relevant replies for chatbots or digital assistants. - Seamless integration of AI workflows: Combine moderation and text generation tools to build smart, automated pipelines tailored to your product’s needs. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `AI_ML_API_CANCEL_RUN` | Cancel Run | Tool to cancel a run that is currently in progress. Use when you need to stop an assistant run before it completes. | | `AI_ML_API_CREATE_ASSISTANT` | Create Assistant | Tool to create an AI assistant with configurable model, instructions, and tools. Use when you need to set up a new assistant that can maintain conversation context and use tools like code_interpreter or file_search. | | `AI_ML_API_CREATE_MESSAGE` | Create Message | Tool to create a new message in a thread. Use when you need to add a user or assistant message to an existing conversation thread. | | `AI_ML_API_CREATE_RUN` | Create Run | Tool to create a run that executes an assistant on a thread. The assistant processes messages in the thread and generates responses based on its instructions and available tools. | | `AI_ML_API_CREATE_THREAD` | Create Thread | Tool to create a new thread for conversation with an assistant. Threads store messages and maintain context across interactions. Use when starting a new conversation or when you need a fresh context for assistant interactions. | | `AI_ML_API_DELETE_ASSISTANT` | Delete Assistant | Tool to delete an assistant by ID. Use when you need to remove an assistant that is no longer needed. | | `AI_ML_API_DELETE_MESSAGE` | Delete Message | Tool to delete a specific message from a thread. Use when you need to remove an unwanted or erroneous message from a conversation thread. | | `AI_ML_API_DELETE_THREAD` | Delete Thread | Tool to delete a thread by its ID. Use when you need to remove an existing thread from the system. | | `AI_ML_API_GET_ASSISTANT` | Get Assistant | Tool to retrieve details of a specific assistant by ID. Use when you need to fetch configuration, model settings, instructions, or available tools for an existing assistant. | | `AI_ML_API_GET_BILLING_BALANCE` | Get Billing Balance | Tool to retrieve the current billing balance for the account. Use when you need to check available credits, balance status, or auto-debit configuration. | | `AI_ML_API_GET_LUMA_GENERATION` | Get Luma Generation | Tool to fetch Luma AI video generation results by generation IDs. Use after creating a generation to check its status and retrieve the generated video URL when completed. | | `AI_ML_API_GET_MESSAGE` | Get Message | Tool to retrieve information about a specific message by its ID. Use when you need to fetch details of a particular message in a thread. | | `AI_ML_API_GET_RESPONSE` | Get Response by ID | Tool to retrieve a previously generated model response by its unique ID. Use when you need to access details of a specific response, including its output, status, and usage statistics. | | `AI_ML_API_GET_RUN` | Get Run | Tool to retrieve a specific run by ID from a thread. Use when you need to check the status, results, or details of a previously created run. | | `AI_ML_API_GET_RUN_STEP` | Get Run Step | Tool to retrieve a specific run step by its ID within a thread and run. Use when you need detailed information about a particular step's execution status and results. | | `AI_ML_API_GET_THREAD` | Get Thread | Tool to retrieve information about a specific thread by ID. Use when you need to fetch thread details, metadata, or available tool resources for an existing conversation thread. | | `AI_ML_API_LIST_ASSISTANTS` | List Assistants | Tool to list all assistants associated with the account. Use when you need to retrieve available assistants with pagination support. | | `AI_ML_API_LIST_BATCHES` | List Batches | Tool to get the status or results of a batch processing job. Use when you need to check the progress or retrieve results of a previously submitted batch. | | `AI_ML_API_LIST_LUMA_GENERATIONS` | List Luma AI Generations | Tool to fetch user's Luma AI video generations. Use when you need to retrieve a list of all Luma AI video generation tasks for the authenticated user. | | `AI_ML_API_LIST_MESSAGES` | List Thread Messages | Tool to retrieve a list of messages from a specific thread. Use when you need to fetch conversation history or message content from an AI assistant thread. | | `AI_ML_API_LIST_MODELS` | List Models | Tool to list all available AI models from the AI/ML API. Use when you need to retrieve the complete catalog of 400+ models including chat, image, video, voice, and other model types. | | `AI_ML_API_LIST_MODELS_WITH_DETAILS` | List Models With Details | Tool to list all available AI/ML models with detailed information including pricing, features, and capabilities. Use when you need to discover available models or get comprehensive model metadata. | | `AI_ML_API_LIST_RUNS` | List Runs | Tool to list all runs for a specific thread. Use when you need to retrieve runs with pagination support. | | `AI_ML_API_LIST_RUN_STEPS` | List Run Steps | Tool to list the steps in a run. Use when you need to retrieve and examine the execution steps of a specific run within a thread. | | `AI_ML_API_SUBMIT_TOOL_OUTPUTS` | Submit Tool Outputs | Tool to submit tool outputs for a run that requires action. Use when a run has status 'requires_action' and needs tool call results to continue execution. | | `AI_ML_API_TEXT_CHAT_COMPLETION` | Text Chat Completion | Tool to generate text completions or chat responses using a specified LLM model. Use after assembling the conversation history to produce the next response. | | `AI_ML_API_UPDATE_ASSISTANT` | Update Assistant | Tool to modify an existing assistant's properties including name, instructions, model, and tools. Use when you need to update an assistant's configuration or behavior after it has been created. | | `AI_ML_API_UPDATE_MESSAGE` | Update Message | Tool to modify metadata for a specific message in a thread. Use when you need to update message metadata such as tags or custom fields. | | `AI_ML_API_UPDATE_RUN` | Update Run | Tool to update a run's metadata with key-value pairs. Use when you need to attach or modify additional information for a specific run. | | `AI_ML_API_UPDATE_THREAD` | Update Thread | Tool to update thread metadata and tool resources in the AI/ML API. Use when you need to modify existing thread properties or attach resources. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Ai ml api MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ai ml api. It provides structured and secure access so your agent can perform Ai ml api 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 Ai ml api 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 Ai ml api-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: ["ai_ml_api"], }); 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 Ai ml api 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 ai_ml_api, 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 Ai ml api 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: ["ai_ml_api"], }); 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 ai_ml_api, 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 Ai ml api 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 Ai ml api MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/ai_ml_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ai_ml_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ai_ml_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ai_ml_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ai_ml_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ai_ml_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ai_ml_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ai_ml_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/ai_ml_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ai_ml_api/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/ai_ml_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/ai_ml_api/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/ai_ml_api/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. - [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. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Ai ml api MCP? With a standalone Ai ml api MCP server, the agents and LLMs can only access a fixed set of Ai ml api tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ai ml api 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 Ai ml api tools. ### Can I manage the permissions and scopes for Ai ml api while using Tool Router? Yes, absolutely. You can configure which Ai ml api 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 Ai ml api data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)