How to integrate CompanyCam MCP with Vercel AI SDK v6

This guide walks you through connecting CompanyCam to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working CompanyCam agent that can create your projects, add project photos, tag your photos, create project tasks, and add comments through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a CompanyCam account through Composio's CompanyCam MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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CompanyCam is a photo-first jobsite management platform for project teams. It helps crews document work, coordinate jobs, and share visual updates fast.

24 Tools

Introduction

This guide walks you through connecting CompanyCam to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working CompanyCam agent that can create your projects, add project photos, tag your photos, create project tasks, and add comments through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a CompanyCam account through Composio's CompanyCam MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate CompanyCam with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with CompanyCam integration
  • Using Composio's Tool Router to dynamically load and access CompanyCam 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 CompanyCam MCP server, and what's possible with it?

The CompanyCam MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CompanyCam account. It provides structured and secure access to your projects, customers, jobsite photos, tasks, comments, labels, tags, and users, so your agent can create projects, document jobsites, coordinate tasks, organize photos, and share project updates on your behalf.

  • Project and customer management: Have your agent create, search, review, and update projects and customer records, including addresses, contact details, and notes.
  • Jobsite photo documentation: Direct your agent to add photos from public web links, review project photos, update photo descriptions, and organize images with tags.
  • Task coordination: Let the agent create, review, and update project tasks, track completion status, and assign available team members.
  • Project communication: Instruct your agent to add comments to projects or photos and review existing discussions before sharing an update.
  • Project organization: Have your agent apply project labels, create reusable photo tags, and find projects using lifecycle, assignee, or text-based searches.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

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
4

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

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
5

Import required modules and validate environment

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,
});
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
6

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["companycam"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to CompanyCam 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 CompanyCam-related tools through the MCP protocol
7

Connect to MCP server and retrieve tools

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();
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 CompanyCam tools that the agent can use
8

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to companycam, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
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
9

Handle user input and stream responses with real-time tool feedback

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);
});
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 CompanyCam 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

Complete Code

Here's the complete code to get you started with CompanyCam and Vercel AI SDK:

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: ["companycam"],
  });

  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 companycam, 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 CompanyCam 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.
TOOLS

Supported Tools

Every CompanyCam action and event your agent gets out of the box.

Add Comment

Add a comment to either a CompanyCam project or photo.

Add Photo Tags

Attach media tags by display name.

Add Project Labels

Attach project labels by display name.

Add Project Photo From URL

Add a photo to a CompanyCam project by having CompanyCam fetch a publicly reachable HTTPS image URL.

Create Customer

Create a CompanyCam customer with optional contact details, company status, notes, address, and initial contacts.

Create Project

Create a CompanyCam project with its required address and optional contact, customer, location, and template details.

Create Project Task

Create a task on a CompanyCam project with task details and optional assignees.

Create Photo Tag

Create a reusable CompanyCam media tag for organizing photos.

Get Account Context

Return the connected CompanyCam user and company so an agent can identify the account, role, and company before acting.

Get Photo

Get one CompanyCam photo by ID.

Get Project

Get one CompanyCam project by ID.

List Comments

List comments attached to either a CompanyCam project or photo.

List Customers

List or search CompanyCam customers by name or contact text.

List Project Labels

List reusable CompanyCam project labels that can be attached to projects.

List Project Photos

List and filter photos in a CompanyCam project by tags, users, or groups, one cursor page at a time.

List Projects

List projects with optional lifecycle and assigned-user filters, one cursor page at a time.

List Project Tasks

List tasks for a CompanyCam project, including status, completion, assignees, and attached media.

List Photo Tags

List reusable CompanyCam media tags and return an opaque cursor for the next page.

List Users

List CompanyCam users available for task assignment and project workflows.

Search Projects

Search CompanyCam projects by a free-text query when project list filters are insufficient.

Set Photo Description

Create or replace the description attached to a CompanyCam photo.

Update Customer

Update the name, notes, city, or postal code of an existing CompanyCam customer.

Update Project

Update editable project identity, address, or primary-contact fields without changing archive state.

Update Project Task

Update a CompanyCam project task's details or add and retain assignees.

FRAMEWORKS

How to build CompanyCam MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use CompanyCam MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use CompanyCam MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use CompanyCam MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use CompanyCam MCP with Claude Cowork

Codex logo

Codex

Use CompanyCam MCP with Codex

OpenClaw logo

OpenClaw

Use CompanyCam MCP with OpenClaw

Hermes logo

Hermes

Use CompanyCam MCP with Hermes

CLI logo

CLI

Use CompanyCam MCP with CLI

Google ADK logo

Google ADK

Use CompanyCam MCP with Google ADK

LangChain logo

LangChain

Use CompanyCam MCP with LangChain

Mastra AI logo

Mastra AI

Use CompanyCam MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use CompanyCam MCP with LlamaIndex

CrewAI logo

CrewAI

Use CompanyCam MCP with CrewAI

FAQ

Frequently asked questions

With a standalone CompanyCam MCP server, the agents and LLMs can only access a fixed set of CompanyCam tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from CompanyCam and many other apps based on the task at hand, all through a single MCP endpoint.

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 CompanyCam tools.

Yes, absolutely. You can configure which CompanyCam 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.

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 CompanyCam data and credentials are handled as safely as possible.

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