How to Use the UTM Campaign Builder MCP in Vercel AI SDK

Build flawless UTM tracking links directly into your UI with the Vercel AI SDK.

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Connect UTM Campaign Builder MCP to Vercel AI SDK

Create your Vinkius account to connect UTM Campaign Builder 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 UTM Campaign Builder with Vercel AI SDK

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Generate Encoded Tracking URLs in Real Time

The `build_utm_url` tool safely constructs and encodes marketing UTM tracking URLs. Your agent gets perfect links instantly, so you don't have to worry about manual encoding or broken query strings. This means your end user sees the data appear live as your AI client streams results into React or Next.js. No more loading spinners when generating campaign links.

Keep Marketing Agents from Hallucinating Broken Links

Don't let your agent mess up critical tracking parameters. The `build_utm_url` tool requires the base landing page URL, source, and medium to build a link. It handles all query string merging safely for you. This function is built specifically to prevent broken Google Analytics routing—a huge win when building user-facing products with your AI client.

Support Optional UTM Parameters

Need more detail than just source and medium? You can optionally add campaign, term, or content parameters via `build_utm_url`. The MCP Server manages all the encoding behind the scenes. This makes it easy to pass complex tracking requirements directly from your AI client into the user interface without writing extra cleanup code.

Setup guide

Set up UTM Campaign Builder 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 UTM Campaign Builder 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 UTM Campaign Builder transactions",
});

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

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Common questions about UTM Campaign Builder MCP in Vercel AI SDK

It handles all URL encoding and query string merging safely. You just pass the components (source, medium, etc.), and the MCP Server builds a clean link for your AI client. This prevents broken tracking.

This server only touches URL strings. It constructs marketing URLs based on required source, medium, and optional campaign/term parameters. The data is purely string-based tracking information.

Yes, because it safely constructs the full query string in one go. You provide the base URL and all necessary parameters via `build_utm_url`, ensuring the final link is ready to track multiple steps correctly.

You use the optional parameters—campaign, term, and content—when calling `build_utm_url`. The tool ensures those specific components are correctly added and encoded into the final URL.

Absolutely. By handling all URL encoding internally, it prevents your agent from accidentally creating broken or malformed query strings that could compromise tracking accuracy.

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