How to Use the Elasticsearch Vector MCP in Vercel AI SDK

Q: How do I run an Elasticsearch Vector search with the Vercel AI SDK?

Call mcpClient.tools() to get the search tool and pass it directly to streamText. Your AI agent will execute the kNN query on your cluster and stream the matching documents back to your frontend.

Q: Can I create new vector mappings using Elasticsearch Vector and Vercel AI SDK?

Yes, you use the createindex tool within your Vercel AI SDK setup. The agent checks the cluster status and configures the densevector fields automatically.

Q: How do I handle document deletions using Elasticsearch Vector in Vercel AI SDK?

You invoke the deletedocument tool. Your agent identifies the document ID that needs to go, triggers the deletion, and updates the search index instantly.

Q: Does this MCP Server support high-dimensional embeddings?

It does. The createindex tool configures the exact dimension size your model outputs, allowing the search tool to run fast kNN calculations on your cluster.

Get raw vector search results from your Elasticsearch Vector index directly to your React UI in real-time with the Vercel AI SDK.

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Setup Elasticsearch Vector with Vercel AI SDK

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Instant real-time vector search streaming

The `search` tool finds nearest neighbors in your Elasticsearch cluster and sends the raw vectors straight to your Vercel AI SDK stream via the MCP. You do not have to wait for a heavy backend payload to resolve before showing your users the closest matches. Your frontend gets immediate access to the matching documents. This means your React or Next.js components render the search results chunk by chunk as they arrive from the Elasticsearch cluster.

Dynamic index creation via the Vercel AI SDK MCP Server

Running the `create_index` tool lets your agent set up dense vector mappings on the fly. You run this command inside your Edge Functions to ensure your Elasticsearch cluster is prepared for high-dimensional vectors before you start pushing data. If you need to check if an index exists, the `get_index` and `list_indexes` tools give you the exact schema details. The Vercel AI SDK handles these tool calls natively, making index management a background task that doesn't block your main user thread.

Direct document ingestion from the edge

Using the `index_document` tool, you can write raw text and embeddings directly into your Elasticsearch index from any Edge route. You do not need a separate database driver or a complex ingestion worker to keep your vectors fresh. When users delete content in your app, your agent runs `delete_document` to remove that vector instantly. This MCP integration keeps your search results clean and prevents stale embeddings from showing up in your UI.

Setup guide

Set up Elasticsearch Vector 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 Elasticsearch Vector 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 Elasticsearch Vector 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 Elasticsearch Vector. 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 Elasticsearch Vector MCP in Vercel AI SDK

Call `mcpClient.tools()` to get the `search` tool and pass it directly to `streamText`. Your AI agent will execute the kNN query on your cluster and stream the matching documents back to your frontend.

Yes, you use the `create_index` tool within your Vercel AI SDK setup. The agent checks the cluster status and configures the dense_vector fields automatically.

You invoke the `delete_document` tool. Your agent identifies the document ID that needs to go, triggers the deletion, and updates the search index instantly.

It does. The `create_index` tool configures the exact dimension size your model outputs, allowing the `search` tool to run fast kNN calculations on your cluster.

All vector coordinates, index configurations, and raw document payloads pass through an isolated V8 sandbox on Vinkius. Your sensitive embedding vectors and index mappings never persist on our servers, as we only pipe the data directly to your designated Elasticsearch endpoint.

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