How to Use the HTML DOM Query Engine MCP in Vercel AI SDK

Q: How do I install the HTML DOM Query Engine for my Vercel AI SDK project?

Run npm install ai @ai-sdk/mcp to add the MCP client library. Next, use the createMCPClient function to connect to the HTTP transport endpoint. You then pass the tools directly into streamText or generateText.

Q: How does the HTML DOM Query Engine prevent token waste in Vercel AI SDK?

The querydom tool filters raw HTML down to specific CSS targets before sending data to the model. This means your Vercel AI SDK application only processes highly relevant text. You avoid paying for thousands of lines of useless boilerplate code.

Feed raw HTML parsing results directly into your React components with the Vercel AI SDK.

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Connect HTML DOM Query Engine MCP to Vercel AI SDK

Create your Vinkius account to connect HTML DOM Query Engine 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 HTML DOM Query Engine with Vercel AI SDK

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Real-Time DOM Parsing with Vercel AI SDK

The `query_dom` tool allows your agent to extract specific text or attributes from raw HTML strings using standard CSS selectors. When you hook this up to your Vercel AI SDK setup, the extracted data streams straight to your React or Next.js frontend. You do not have to wait for the entire scraping job to finish before updating your UI components. This immediate feedback loop keeps your interface feeling incredibly fast. Your users watch the raw HTML data transform into clean, rendered elements on their screen as the agent processes the payload.

Edge-Compatible HTML Scraping

The `query_dom` tool runs on our hosted MCP Server within lightweight Edge Functions because it avoids heavy headless browser overhead. This setup lets your Vercel AI SDK application process massive HTML payloads without hitting cold-start delays. It keeps your compute costs low while maintaining fast response times. Deploying this on global edge networks ensures your parsing logic runs close to your users. You get fast DOM queries without provisioning heavy server infrastructure.

Deterministic Data Extraction

The `query_dom` tool processes HTML structures deterministically to return exactly what your CSS selector matches. By passing these clean extractions directly to your Vercel AI SDK text generators, you prevent the LLM from hallucinating page structure. It only works with the actual text or attributes found in the DOM. This approach cuts down on token usage significantly. You only send the precise scraped content to the model instead of dumping raw, noisy HTML markup into the prompt context.

Setup guide

Set up HTML DOM Query Engine 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 HTML DOM Query Engine 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 HTML DOM Query Engine transactions",
});

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

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Common questions about HTML DOM Query Engine MCP in Vercel AI SDK

Run `npm install ai @ai-sdk/mcp` to add the MCP client library. Next, use the `createMCPClient` function to connect to the HTTP transport endpoint. You then pass the tools directly into `streamText` or `generateText`.

Yes, the server processes large payloads efficiently without hitting memory limits. Because the `query_dom` tool runs on the Vinkius managed sandbox, your edge function avoids high memory consumption. It only receives the final extracted text or attributes.

The `query_dom` tool filters raw HTML down to specific CSS targets before sending data to the model. This means your Vercel AI SDK application only processes highly relevant text. You avoid paying for thousands of lines of useless boilerplate code.

No, this MCP Server uses a fast, native parser that does not require Puppeteer or Playwright. You simply pass the raw HTML string and your CSS selector directly to the tool. It returns clean text or attributes instantly.

All raw HTML strings and CSS selectors are processed inside an ephemeral, zero-trust V8 Isolate sandbox. Vinkius does not store or log your payload contents. Once the tool returns the extracted text, the sandbox session immediately terminates.

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