How to Use the Dopplio MCP in Vercel AI SDK

Q: How do I connect the Dopplio MCP Server to my Vercel AI SDK project?

Install the packages using npm install ai @ai-sdk/mcp first. Next, initialize the client using createMCPClient with your HTTP transport URL, then pass mcpClient.tools() directly to your streamText function. Remember to call mcpClient.close() when the session ends to prevent memory leaks.

Q: How does my Vercel AI SDK agent track multiple Dopplio video renders at once?

Your agent uses listvideos to get a complete history of all generated files. It can then filter this list or iterate through specific IDs using getrenderstatus to update your database.

Q: Does the Vercel AI SDK handle PDF generation?

Yes, you can invoke the renderpdf tool using the MCP interface. The agent triggers the render and returns the document URL directly to your streaming chat component.

Build real-time video personalization interfaces that render clips on the fly using Vercel AI SDK.

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Connect Dopplio MCP to Vercel AI SDK

Create your Vinkius account to connect Dopplio to Vercel AI SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Dopplio with Vercel AI SDK

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Generate video clips directly inside your Vercel AI SDK UI.

The `generate_video` tool lets your agent start rendering personalized video clips directly from a user prompt. Instead of making users wait behind a static spinner, the Vercel AI SDK streams the progress of the render job so your frontend shows live updates as the video builds. You pass the template ID and personalization data straight to the LLM. The agent calls the tool, and your UI updates instantly with the render status, keeping users engaged while the backend does the heavy lifting.

Capture live webpage screenshots during chat sessions.

The `capture_screenshot` tool grabs a clean image of any URL your user inputs during an active chat session. Your application can immediately display this visual preview in the chat feed while the agent works on the next step. This eliminates the delay of waiting for full background processes to complete. Your frontend gets the visual asset immediately, allowing the AI to reference the actual page layout in its next response.

Keep users updated with live rendering status.

The `get_render_status` tool checks the progress of any active video generation job in real-time. Because you are using this MCP Server with Vercel's streaming capabilities, your UI can poll this status without blocking the main text generation thread. Once the status changes to complete, the agent grabs the final URL using `get_video_details`. Your users see their personalized video appear in the chat interface the second it is ready.

Setup guide

Set up Dopplio MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Dopplio tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Dopplio transactions",
});

console.log(text);
await mcpClient.close();

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dopplio. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Dopplio MCP in Vercel AI SDK

Install the packages using `npm install ai @ai-sdk/mcp` first. Next, initialize the client using `createMCPClient` with your HTTP transport URL, then pass `mcpClient.tools()` directly to your `streamText` function. Remember to call `mcpClient.close()` when the session ends to prevent memory leaks.

Yes, this setup is compatible with Edge runtimes. The MCP client connects over lightweight HTTP, meaning you do not have to worry about node-specific dependencies slowing down your Edge runtimes.

Your agent uses `list_videos` to get a complete history of all generated files. It can then filter this list or iterate through specific IDs using `get_render_status` to update your database.

Yes, you can invoke the `render_pdf` tool using the MCP interface. The agent triggers the render and returns the document URL directly to your streaming chat component.

All HTML strings, URLs, and video personalization inputs run inside secure, ephemeral V8 sandboxes. Your raw data is processed only to generate the requested MP4 or PDF assets and is not stored or used to train models.

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