# How to integrate LeadBoxer MCP with Vercel AI SDK v6 ```json { "title": "How to integrate LeadBoxer MCP with Vercel AI SDK v6", "toolkit": "LeadBoxer", "toolkit_slug": "leadboxer", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/leadboxer/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/leadboxer/framework/ai-sdk.md", "updated_at": "2026-03-29T06:40:06.775Z" } ``` ## Introduction This guide walks you through connecting LeadBoxer to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working LeadBoxer agent that can list all new leads from today, show all companies visiting pricing page, get contact info for most recent lead through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a LeadBoxer account through Composio's LeadBoxer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate LeadBoxer with - [ChatGPT](https://composio.dev/toolkits/leadboxer/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/leadboxer/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/leadboxer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/leadboxer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/leadboxer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/leadboxer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/leadboxer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/leadboxer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/leadboxer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/leadboxer/framework/cli) - [Google ADK](https://composio.dev/toolkits/leadboxer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/leadboxer/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/leadboxer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/leadboxer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/leadboxer/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with LeadBoxer integration - Using Composio's Tool Router to dynamically load and access LeadBoxer 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 LeadBoxer MCP server, and what's possible with it? The LeadBoxer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your LeadBoxer account. It provides structured and secure access so your agent can perform LeadBoxer operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `LEADBOXER_ADD_OR_UPDATE_LEAD_TAGS` | Add or Update Lead Tags | Tool to add or update lead tags in LeadBoxer. Use when you need to manage lead tags by adding new tags, removing specific tags, or overwriting all existing tags for a specified user. | | `LEADBOXER_DELETE_CTD` | Delete Custom Tracking Domain | Tool to delete a custom tracking domain entry for a dataset. Use when you need to remove a specific custom tracking domain (CTD) associated with a dataset in LeadBoxer. | | `LEADBOXER_GET_EVENTS` | Get Events | Tool to fetch events for specified sessions from LeadBoxer. Use when you need to retrieve behavioral data including pageviews, clicks, form submissions, email interactions, and custom events. Events can be filtered by session ID, email, user ID, or segment/smartlist IDs. | | `LEADBOXER_GET_LEAD_SCORE_FORMULA` | Get Lead Score Formula | Tool to fetch the lead score formula for a specific dataset. Use when you need to understand how lead scores are calculated for records in a dataset. The formula defines scoring based on criteria types: Range (number ranges), Match (exact values), Exists (presence of values), and Boost (event-based scoring). | | `LEADBOXER_GET_SESSIONS` | Get Sessions | Tool to fetch sessions for a specified lead ID from LeadBoxer. Use when you need to retrieve session data for a user, with optional filtering by email, user ID, Smartlist ID, or segment ID. A session represents a single visit to the site, and users can have multiple sessions over time. | | `LEADBOXER_LOG_SERVER_SIDE_EVENT` | Log Server-Side Event | Tool to track server-side events in LeadBoxer. Use when tracking backend events, page views, or custom activities from your server. This endpoint supports tracking user interactions, conversions, and other activities that occur on the server-side. | | `LEADBOXER_GET_CUSTOM_TRACKING_DOMAINS` | Get Custom Tracking Domains | Tool to fetch custom tracking domain entries for a dataset. Use when you need to retrieve all custom tracking domains that are created or in progress for a specific datasetId. | | `LEADBOXER_POST_EVENT_DATA` | Post Event Data | Tool to send event data for tracking user activities via POST request. Use when tracking events by sending data in the request body with application/x-www-form-urlencoded format. | | `LEADBOXER_GET_LEAD_DETAIL` | Get Lead Detail | Tool to fetch detailed information about a lead based on filters. Use when you need comprehensive lead data for a specific lead ID. The default view type is B2B. Note: On initial pageview, there may be a few seconds overhead before data is populated; consider implementing a 3-second delay after lead creation. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The LeadBoxer MCP server is an implementation of the Model Context Protocol that connects your AI agent to LeadBoxer. It provides structured and secure access so your agent can perform LeadBoxer 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 LeadBoxer 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 LeadBoxer-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: ["leadboxer"], }); 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 LeadBoxer 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 leadboxer, 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 LeadBoxer 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: ["leadboxer"], }); 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 leadboxer, 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 LeadBoxer 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 LeadBoxer MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/leadboxer/framework/chatgpt) - [Antigravity](https://composio.dev/toolkits/leadboxer/framework/antigravity) - [OpenAI Agents SDK](https://composio.dev/toolkits/leadboxer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/leadboxer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/leadboxer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/leadboxer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/leadboxer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/leadboxer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/leadboxer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/leadboxer/framework/cli) - [Google ADK](https://composio.dev/toolkits/leadboxer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/leadboxer/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/leadboxer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/leadboxer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/leadboxer/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and LeadBoxer MCP? With a standalone LeadBoxer MCP server, the agents and LLMs can only access a fixed set of LeadBoxer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from LeadBoxer 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 LeadBoxer tools. ### Can I manage the permissions and scopes for LeadBoxer while using Tool Router? Yes, absolutely. You can configure which LeadBoxer 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 LeadBoxer data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)