# How to integrate Sendspark MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Sendspark MCP with Vercel AI SDK v6",
  "toolkit": "Sendspark",
  "toolkit_slug": "sendspark",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/sendspark/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/sendspark/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:25:25.127Z"
}
```

## Introduction

This guide walks you through connecting Sendspark to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Sendspark agent that can add a new prospect to your latest campaign, list all dynamic video campaigns in workspace, fetch prospect data by email for a campaign through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Sendspark account through Composio's Sendspark MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Sendspark with

- [OpenAI Agents SDK](https://composio.dev/toolkits/sendspark/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/sendspark/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/sendspark/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/sendspark/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/sendspark/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/sendspark/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/sendspark/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/sendspark/framework/cli)
- [Google ADK](https://composio.dev/toolkits/sendspark/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/sendspark/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/sendspark/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/sendspark/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/sendspark/framework/crew-ai)

## TL;DR

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

The Sendspark MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Sendspark account. It provides structured and secure access to your video campaigns and prospect data, so your agent can create campaigns, manage prospects, audit webhooks, and fetch campaign analytics on your behalf.
- Dynamic campaign creation and management: Instantly launch new dynamic video campaigns or fetch details of existing campaigns in your workspace without manual setup.
- Prospect automation at scale: Add individual or multiple prospects to video campaigns, retrieve their details by email, and streamline personalized outreach in seconds.
- Webhook auditing and management: List all configured webhooks or remove outdated ones to keep your integrations secure and up-to-date.
- Campaign analytics and tracking: Retrieve data and performance metrics for your campaigns and prospects to monitor engagement and optimize results.
- API health monitoring: Check Sendspark API health status before making calls, ensuring your automations always run smoothly.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SENDSPARK_ADD_MULTIPLE_PROSPECTS_TO_DYNAMIC_CAMPAIGN` | Add Multiple Prospects to Dynamic Campaign | Tool to add multiple prospects to a dynamic campaign in bulk. Use when you need to add many prospects to your dynamic video campaign at once after confirming associated charges. |
| `SENDSPARK_ADD_PROSPECT_TO_DYNAMIC_VIDEO_CAMPAIGN` | Add Prospect to Dynamic Video Campaign | Tool to add a prospect to a dynamic video campaign. Use after confirming workspace and campaign IDs. Example: Add new prospect with name/contact details to dynamic "dyn12345" under a known workspace. |
| `SENDSPARK_API_HEALTH_STATUS` | API Health Status | Tool to check the health status of the Sendspark API. Use before making other API calls to ensure the service is up. |
| `SENDSPARK_CREATE_DYNAMIC_VIDEO_CAMPAIGN2` | Create Dynamic Video Campaign V2 | Tool to create a dynamic video campaign in a workspace. Use when you need to create a container for AI-personalized dynamic videos that can be sent to prospects. |
| `SENDSPARK_DELETE_WEBHOOK` | Delete Webhook | Delete a webhook by its unique ID. Returns a structured response with status code and message. This action is idempotent: deleting a non-existent webhook (404) with workspaceId provided returns success. Invalid webhook IDs return 400 with error details. Best practice: Always provide workspaceId to use the reliable workspace-scoped endpoint. |
| `SENDSPARK_GET_DYNAMIC_CAMPAIGN_BY_ID` | Get Dynamic Campaign by ID | Tool to retrieve details of a specific dynamic video campaign. Use after confirming workspace and campaign IDs. |
| `SENDSPARK_GET_WORKSPACE_PROSPECT_DATA_BY_EMAIL` | Get Workspace Prospect Data by Email | Tool to retrieve prospect data by email in a dynamic campaign. Use after adding a prospect to a campaign to fetch its details. |
| `SENDSPARK_LIST_DYNAMIC_VIDEO_CAMPAIGNS` | List Dynamic Video Campaigns | Tool to list all dynamic video campaigns in a workspace. Use when retrieving campaigns with optional pagination, filtering, or search. |
| `SENDSPARK_LIST_WEBHOOKS` | List Webhooks | Retrieves all configured webhooks for a Sendspark workspace. Webhooks are automated notifications sent when specific events occur in dynamic video campaigns (e.g., video created, video played, CTA clicked, video opened). Use this action to audit active webhook configurations, verify webhook URLs, or check which events are being monitored. Returns an empty list if no webhooks are configured. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Sendspark MCP server is an implementation of the Model Context Protocol that connects your AI agent to Sendspark. It provides structured and secure access so your agent can perform Sendspark 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 Sendspark 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 Sendspark-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: ["sendspark"],
  });

  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 Sendspark 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 sendspark, 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 Sendspark 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: ["sendspark"],
  });

  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 sendspark, 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 Sendspark 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 Sendspark MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/sendspark/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/sendspark/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/sendspark/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/sendspark/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/sendspark/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/sendspark/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/sendspark/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/sendspark/framework/cli)
- [Google ADK](https://composio.dev/toolkits/sendspark/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/sendspark/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/sendspark/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/sendspark/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/sendspark/framework/crew-ai)

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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Sendspark MCP?

With a standalone Sendspark MCP server, the agents and LLMs can only access a fixed set of Sendspark tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Sendspark 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 Sendspark tools.

### Can I manage the permissions and scopes for Sendspark while using Tool Router?

Yes, absolutely. You can configure which Sendspark 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 Sendspark data and credentials are handled as safely as possible.

---
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