# How to integrate Procfu MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Procfu MCP with Vercel AI SDK v6", "toolkit": "Procfu", "toolkit_slug": "procfu", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/procfu/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/procfu/framework/ai-sdk.md", "updated_at": "2026-05-06T08:24:38.041Z" } ``` ## Introduction This guide walks you through connecting Procfu to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Procfu agent that can find new entries in updated google sheet, delete a specific file from google drive, generate random test users for qa through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Procfu account through Composio's Procfu MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Procfu with - [OpenAI Agents SDK](https://composio.dev/toolkits/procfu/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/procfu/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/procfu/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/procfu/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/procfu/framework/codex) - [OpenClaw](https://composio.dev/toolkits/procfu/framework/openclaw) - [Hermes](https://composio.dev/toolkits/procfu/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/procfu/framework/cli) - [Google ADK](https://composio.dev/toolkits/procfu/framework/google-adk) - [LangChain](https://composio.dev/toolkits/procfu/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/procfu/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/procfu/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/procfu/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Procfu integration - Using Composio's Tool Router to dynamically load and access Procfu 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 Procfu MCP server, and what's possible with it? The Procfu MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Procfu account. It provides structured and secure access to advanced Podio automations, letting your agent compare arrays, generate test data, interact with Google Drive and Sheets, and even harness OpenAI for creative tasks—all on your behalf. - Automated data comparison and manipulation: Have your agent find differences, additions, deletions, or intersections between two JSON arrays to quickly analyze data changes or synchronize lists. - Google Drive file management: Direct your agent to delete files or folders from your Google Drive, streamlining cleanup and organization without manual effort. - Dynamic test data and placeholder generation: Instantly generate dummy emails, numbers, images, or addresses for testing, prototyping, or populating demo environments. - Retrieve Google Sheets data: Ask your agent to pull contents from a specific Google Sheet as an array, making it easy to process, analyze, or migrate spreadsheet data. - Conversational AI and image generation: Let your agent query OpenAI GPT for answers or generate new images from text prompts, extending automation into creative and cognitive tasks. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PROCFU_ARRAY_DIFF_DEL` | Array Difference Deletions | Tool to return items removed when comparing two JSON arrays. Use when you have two arrays and need to know which elements were deleted. | | `PROCFU_ARRAY_DIFF_NEW` | Array Diff New | Tool to return items added in the second JSON array. Use when you need to identify new elements between two list versions. Example: Compare [1,3,4] vs [1,3,6] to get [6]. | | `PROCFU_ARRAY_DIFF_SAME` | Array Diff Same | Tool to get items present in both JSON arrays. Computes the intersection locally to avoid external API dependency. Rules: - Two items are considered equal if their JSON representations match (with sorted keys for objects). - The result contains unique items present in both arrays, preserving the order they appear in json_array_b. | | `PROCFU_ARRAY_SORT` | Array Sort | Tool to sort a JSON array of values. Use when you need to order elements ascending or descending. | | `PROCFU_DELETE_GOOGLE_DRIVE` | Delete Google Drive | Tool to delete a Google Drive file or folder. Use after obtaining a valid Google Drive ID. | | `PROCFU_DUMMY_DATA` | Generate dummy data | Tool to generate dummy data. Use when you need random emails, text, numbers, dates, people, addresses, or images for testing or placeholder data. | | `PROCFU_GOOGLE_DRIVE_DELETE` | Google Drive Delete | Tool to delete a Google Drive file or folder. Use after obtaining a valid Google Drive ID. | | `PROCFU_OPEN_AI_GPT` | Ask question to OpenAI GPT | Tool to ask a question to OpenAI GPT. Use when you need a conversational answer from GPT. | | `PROCFU_OPEN_AI_IMAGE` | Generate Image with OpenAI | Tool to generate an image via OpenAI API. Use when you need programmatic image creation from a text prompt. | | `PROCFU_SHEETS_GET` | Get Google Sheet contents as array | Tool to get sheet contents as array. Use when you need to retrieve Google Sheet data as an associative array. | | `PROCFU_SHEETS_GET_METADATA` | Get Google Sheets Metadata | Tool to retrieve metadata of a Google Sheets spreadsheet, including sheet names, IDs, and properties. Use when you need sheet-level details for a given spreadsheet ID. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Procfu MCP server is an implementation of the Model Context Protocol that connects your AI agent to Procfu. It provides structured and secure access so your agent can perform Procfu 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 Procfu 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 Procfu-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: ["procfu"], }); 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 Procfu 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 procfu, 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 Procfu 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: ["procfu"], }); 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 procfu, 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 Procfu 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 Procfu MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/procfu/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/procfu/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/procfu/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/procfu/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/procfu/framework/codex) - [OpenClaw](https://composio.dev/toolkits/procfu/framework/openclaw) - [Hermes](https://composio.dev/toolkits/procfu/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/procfu/framework/cli) - [Google ADK](https://composio.dev/toolkits/procfu/framework/google-adk) - [LangChain](https://composio.dev/toolkits/procfu/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/procfu/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/procfu/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/procfu/framework/crew-ai) ## Related Toolkits - [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. 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It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps. - [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination. - [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code. - [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Procfu MCP? With a standalone Procfu MCP server, the agents and LLMs can only access a fixed set of Procfu tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Procfu 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 Procfu tools. ### Can I manage the permissions and scopes for Procfu while using Tool Router? Yes, absolutely. You can configure which Procfu 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 Procfu data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)