How to Use the AdButler MCP in Vercel AI SDK

Q: How do I initialize the AdButler MCP Server inside a Next.js Edge Route with Vercel AI SDK?

Install @ai-sdk/mcp and run createMCPClient pointing to your Vinkius endpoint. Pass the output of mcpClient.tools() into generateText or streamText inside your route handler.

Q: What happens if the AdButler API goes down during a live stream?

Your agent can run checkadbutlerstatus first to verify the API is responsive. If the check fails, the SDK handles the error gracefully, allowing your UI to show a clean fallback state.

Stream real-time AdButler ad performance stats directly to your Next.js frontend with Vercel AI SDK.

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Connect AdButler MCP to Vercel AI SDK

Create your Vinkius account to connect AdButler 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 AdButler with Vercel AI SDK

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Live AdButler performance streams via Vercel AI SDK

The `get_stats` and `list_campaigns` tools fetch real-time ad performance metrics directly from your account. Your users shouldn't stare at loading spinners while waiting for ad performance reports. When your agent calls these tools, the Vercel AI SDK streams those raw metrics straight into your React components in real time. This setup bypasses the usual API-to-backend-to-frontend lag. By passing the tools from this MCP Server directly into `streamText`, you render live impressions, clicks, and CTR data as the model fetches them.

Interactive AdButler directories built on the edge

The `list_advertisers`, `list_publishers`, and `list_zones` tools retrieve structural configurations of your ad network. Stop writing manual fetch wrappers for every single AdButler list endpoint. Feed these tools straight to your Edge-compatible AI client. The model reads the layout requirements, calls the tools, and displays the structured zones or publishers instantly. You get clean TypeScript types and zero cold-start penalties when running on Vercel.

UI-driven banner audits with this MCP Server

The `list_banners` and `list_placements` tools pull active creatives and target locations for auditing. Let your users inspect active creatives without digging through the AdButler dashboard. Your agent uses these tools to pull active image assets and target zones, rendering them immediately in your UI. Remember to run `mcpClient.close()` when the stream finishes to keep your Edge Functions light and leak-free.

Setup guide

Set up AdButler 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 AdButler 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 AdButler transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AdButler. 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 AdButler MCP in Vercel AI SDK

Store your AdButler API key as an environment variable on Vercel. Pass it through the `authProvider` configuration during `createMCPClient` initialization to keep your keys safe on the server side.

Yes, that is the main benefit of this setup. When your client triggers `get_stats`, the raw JSON payload streams directly into your UI components via `streamText` without waiting for a full page reload.

Install `@ai-sdk/mcp` and run `createMCPClient` pointing to your Vinkius endpoint. Pass the output of `mcpClient.tools()` into `generateText` or `streamText` inside your route handler.

Your agent can run `check_adbutler_status` first to verify the API is responsive. If the check fails, the SDK handles the error gracefully, allowing your UI to show a clean fallback state.

No, Vinkius runs on a zero-trust V8 sandbox that processes your AdButler advertiser metadata, campaign IDs, and serving stats entirely in memory. No data is stored or logged on our servers after the execution context closes.

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