How to Use the Google Analytics MCP in Vercel AI SDK

Q: How do I set up the Google Analytics MCP Server with Vercel AI SDK?

Install @ai-sdk/mcp and ai, then configure createMCPClient with your HTTP endpoint. Pass the tools directly to streamText and always call mcpClient.close() when the request finishes.

Q: Can I handle user authentication dynamically in Google Analytics queries with Vercel AI SDK?

Yes, configure the authProvider option in your createMCPClient setup to pass user-specific GA4 tokens. This allows the Vercel AI SDK to pull property data from the logged-in user's account securely.

Q: Can the Vercel AI SDK stream user activity logs?

The getuseractivity tool fetches individual user histories and streams the raw event logs straight to your UI. This helps you build real-time customer support interfaces that show exactly what a user did before opening a ticket.

Get real-time Google Analytics metrics streaming directly into your React components with the Vercel AI SDK.

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Connect Google Analytics MCP to Vercel AI SDK

Create your Vinkius account to connect Google Analytics 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 Google Analytics with Vercel AI SDK

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Live GA4 streaming in Vercel AI SDK

The `run_realtime_report` tool pulls active user counts and page views from the last 30 minutes to feed your streaming frontend. Your AI client invokes this tool through the dynamic MCP setup, letting you render live traffic widgets directly in Next.js. Instead of waiting for a full payload to resolve, the Vercel AI SDK streams the incoming JSON chunks to the client. This means users watch their current dashboard metrics update block-by-block as the server fetches them.

Batch report rendering

The `batch_run_reports` tool pulls multiple data arrays in a single round-trip to prevent UI flickering on complex dashboards. Your agent maps these results directly to your React chart components using `streamText` for immediate rendering. We use this tool to bypass the usual multi-request bottleneck on the Edge. You initialize the client, grab the tools array, and let the model coordinate complex GA4 metrics without writing custom API routes using this MCP Server.

Prevent runtime errors with compatibility checks

The `check_compatibility` tool validates your chosen GA4 dimensions and metrics before the Vercel AI SDK sends the query to Google. This keeps your edge functions from failing midway through a stream due to invalid dimension combinations. Your agent calls this validation tool first, then proceeds to `run_report` once it gets the green light. This workflow ensures that your dynamic UI components never render half-broken charts.

Setup guide

Set up Google Analytics 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 Google Analytics 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 Google Analytics 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 Google Analytics 4. 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 Google Analytics MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and `ai`, then configure `createMCPClient` with your HTTP endpoint. Pass the tools directly to `streamText` and always call `mcpClient.close()` when the request finishes.

Yes, configure the `authProvider` option in your `createMCPClient` setup to pass user-specific GA4 tokens. This allows the Vercel AI SDK to pull property data from the logged-in user's account securely.

The SDK relies on your local rate-limiting middleware to throttle requests before they hit the Google Analytics API. You can also run `check_compatibility` to avoid wasting API tokens on invalid queries.

The `get_user_activity` tool fetches individual user histories and streams the raw event logs straight to your UI. This helps you build real-time customer support interfaces that show exactly what a user did before opening a ticket.

This MCP server processes your property IDs and user activity data inside an isolated, zero-trust V8 sandbox. No analytical data or credentials are cached on the Vinkius platform, keeping your raw tracking metrics private.

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