# How to integrate Modelry MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Modelry MCP with Vercel AI SDK v6", "toolkit": "Modelry", "toolkit_slug": "modelry", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/modelry/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/modelry/framework/ai-sdk.md", "updated_at": "2026-05-12T10:19:19.161Z" } ``` ## Introduction This guide walks you through connecting Modelry to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Modelry agent that can list all modeling requests in your workspace, create a new workspace for your models, get details for a specific embed through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Modelry account through Composio's Modelry MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Modelry with - [OpenAI Agents SDK](https://composio.dev/toolkits/modelry/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/modelry/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/modelry/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/modelry/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/modelry/framework/codex) - [OpenClaw](https://composio.dev/toolkits/modelry/framework/openclaw) - [Hermes](https://composio.dev/toolkits/modelry/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/modelry/framework/cli) - [Google ADK](https://composio.dev/toolkits/modelry/framework/google-adk) - [LangChain](https://composio.dev/toolkits/modelry/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/modelry/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/modelry/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/modelry/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Modelry integration - Using Composio's Tool Router to dynamically load and access Modelry 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 Modelry MCP server, and what's possible with it? The Modelry MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Modelry account. It provides structured and secure access to your machine learning model management, so your agent can perform actions like listing modeling requests, creating workspaces, retrieving embed details, and managing products on your behalf. - Workspace management: Easily create new workspaces or fetch details about existing ones to keep your projects organized and separated. - Embed and product operations: List all available embeds, get detailed information, or delete embeds and products as needed for smooth deployment and maintenance. - Repository handling: Retrieve details of product repositories or remove repositories you no longer need—all with structured agent commands. - Modeling request tracking: Quickly list all 3D modeling requests tied to your account to monitor progress and manage workflows efficiently. - Secure automated actions: Let your agent handle repetitive or administrative model management tasks securely, saving you time and effort. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `MODELRY_CREATE_WORKSPACE` | Create Workspace | Create a new workspace or return an existing one with the same name. Workspaces are used to organize products and embeds in Modelry. If workspace creation is not supported by the API, this tool will return an existing workspace matching the requested name. | | `MODELRY_DELETE_EMBED` | Delete Modelry Embed | Tool to delete an embed. Tries multiple common endpoint patterns and treats 404 as idempotent success after exhausting candidates (embed already deleted or not found). | | `MODELRY_DELETE_PRODUCT` | Delete Modelry Product | Permanently deletes a product from Modelry by its ID. Use this action to remove a product that is no longer needed. This operation is idempotent - deleting an already-deleted product will succeed without error. Prerequisites: - Obtain the product ID using MODELRY_LIST_PRODUCTS first - Ensure you have delete permissions for the product WARNING: This action is destructive and cannot be undone. | | `MODELRY_DELETE_PRODUCT_REPOSITORY` | Delete Product Repository | Permanently delete a product repository from Modelry. This action is idempotent - deleting a non-existent repository returns success. Use the list product repositories action first to get valid repository IDs. | | `MODELRY_DELETE_WORKSPACE` | Delete Modelry Workspace | Permanently deletes a Modelry workspace. This action is idempotent - deleting a non-existent workspace will return success. Use the list workspaces action first to get valid workspace IDs. WARNING: This is a destructive action that cannot be undone. | | `MODELRY_GET_EMBED` | Get Embed | Retrieve details of a specific Modelry embed (3D viewer or AR experience for eCommerce). Use MODELRY_LIST_EMBEDS first to obtain valid embed IDs. Returns embed metadata including status, workspace, and configuration details. | | `MODELRY_GET_WORKSPACE` | Get Workspace | Retrieves details for a specific Modelry workspace by its ID or name. The workspace ID can be obtained from the List Workspaces action. This action fetches all workspaces and returns the matching one. | | `MODELRY_LIST_EMBEDS` | List Embeds | List embeds in Modelry. Embeds are 3D viewer/AR embed codes for products. Use to retrieve embed IDs for downstream actions (e.g., MODELRY_GET_EMBED, MODELRY_DELETE_EMBED). Returns empty list if no embeds exist. Supports pagination and optional workspace filtering. | | `MODELRY_LIST_MODELING_REQUESTS` | List Modeling Requests | List all 3D modeling requests in a workspace. Requires workspace_id to scope the request. Returns modeling requests with their status and metadata. | | `MODELRY_LIST_PRODUCT_REPOSITORIES` | List Product Repositories | Tool to list all product repositories in a workspace. Use after confirming the workspace ID. | | `MODELRY_LIST_PRODUCTS` | List Modelry Products | List all products in Modelry. Returns paginated product data including IDs, names, and metadata. Use this to retrieve product IDs needed for other product-related actions like delete or get details. Optionally scope to a specific workspace using workspace_id parameter. | | `MODELRY_LIST_WORKSPACES` | List Modelry Workspaces | Tool to list all workspaces in Modelry. Use when you need to retrieve available workspaces after authenticating. | | `MODELRY_ORDER_MODELING_SERVICE` | Order Modeling Service | Tool to place an order for 3D modeling services. Use when workspace and product IDs are known and modeling specifications are ready. | | `MODELRY_TRACK_MODELING_PROGRESS` | Track Modeling Progress | Tool to track the progress of a 3D modeling request. Use after initiating a modeling job to poll current status and completion percentage. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Modelry MCP server is an implementation of the Model Context Protocol that connects your AI agent to Modelry. It provides structured and secure access so your agent can perform Modelry 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 Modelry 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 Modelry-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: ["modelry"], }); 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 Modelry 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 modelry, 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 Modelry 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: ["modelry"], }); 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 modelry, 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 Modelry 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 Modelry MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/modelry/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/modelry/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/modelry/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/modelry/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/modelry/framework/codex) - [OpenClaw](https://composio.dev/toolkits/modelry/framework/openclaw) - [Hermes](https://composio.dev/toolkits/modelry/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/modelry/framework/cli) - [Google ADK](https://composio.dev/toolkits/modelry/framework/google-adk) - [LangChain](https://composio.dev/toolkits/modelry/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/modelry/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/modelry/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/modelry/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 Modelry MCP? With a standalone Modelry MCP server, the agents and LLMs can only access a fixed set of Modelry tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Modelry 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 Modelry tools. ### Can I manage the permissions and scopes for Modelry while using Tool Router? Yes, absolutely. You can configure which Modelry 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 Modelry data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)