How to Use the Elastic Enterprise Search MCP in Vercel AI SDK

Q: How do I stream Elastic Enterprise Search results with Vercel AI SDK?

Pass the MCP Server tools into generateText or streamText. The agent calls search and streams the JSON response straight to your frontend.

Q: Can Vercel AI SDK manage multiple Elastic Enterprise Search engines?

Yes. The agent uses listengines and getengine to inspect available indices before routing the user's query to the right place.

Q: How do I handle authentication for the MCP server?

Configure the authProvider in createMCPClient. Your Next.js app passes the token, and the MCP client handles the handshake with Elastic.

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Connect Elastic Enterprise Search MCP to Vercel AI SDK

Create your Vinkius account to connect Elastic Enterprise Search 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 Elastic Enterprise Search with Vercel AI SDK

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Stream Elastic Enterprise Search via Vercel AI SDK

The `search` tool connects your AI directly to your Elastic indices. When a user asks a question in your Next.js app, the agent queries the engine and streams the matched documents straight into the UI components. No loading spinners, just raw data appearing instantly. You don't have to build custom API routes for every index. Pass the tools to `streamText`, and your Vercel AI SDK client figures out which engine to hit based on the user's prompt.

Live search analytics and monitoring

The `analytics` tool pulls top queries and click metrics straight from the cluster. You can pipe this data into an admin dashboard, letting your internal agents summarize search trends on the fly. If a specific query starts failing, your agent catches it. It uses `list_engines` to verify the target engine is active, giving you an immediate heads-up before users complain about broken search.

Instant document indexing

The `index_documents` tool pushes new JSON payloads into your target schema directly from the chat interface. A user submits a form, and the agent indexes the record into the database immediately. To confirm the write, the agent runs `list_documents`. The UI updates with the newly indexed content in real-time, proving the data landed exactly where it belongs.

Setup guide

Set up Elastic Enterprise Search 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 Elastic Enterprise Search 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 Elastic Enterprise Search 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 Elastic Enterprise Search. 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 Elastic Enterprise Search MCP in Vercel AI SDK

Pass the MCP Server tools into `generateText` or `streamText`. The agent calls `search` and streams the JSON response straight to your frontend.

Yes. The agent uses `list_engines` and `get_engine` to inspect available indices before routing the user's query to the right place.

It does. The MCP transport uses standard HTTP, so you can call `search` and `index_documents` from edge runtimes without heavy SDK overhead.

Configure the `authProvider` in `createMCPClient`. Your Next.js app passes the token, and the MCP client handles the handshake with Elastic.

The MCP Server routes your JSON payloads strictly to your configured Elastic cluster. Vercel AI SDK processes the `index_documents` payload in memory, but no search queries or document contents are stored by the intermediary gateway.

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