How to Use the Weaviate MCP in Vercel AI SDK

Q: How do I use Weaviate with Vercel AI SDK for vector search?

You call searchnearvector using your query vector. The results stream into the UI immediately, letting users see exactly which data objects match their request in real time.

Q: Can I use Weaviate to get object details with Vercel AI SDK?

Absolutely. Use getobjectdetails by providing a UUID. The resulting metadata streams into your application, letting you build dynamic UIs around specific data points.

Q: What happens if I need the full schema in Vercel AI SDK?

You call getfullschema. This single tool retrieves every class definition from your Weaviate instance. Your agent can then use this map to guide multi-step operations.

Show Weaviate results live in your UI with Vercel AI SDK.

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Connect Weaviate MCP to Vercel AI SDK

Create your Vinkius account to connect Weaviate 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 Weaviate with Vercel AI SDK

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Stream Vector Search Results in Vercel AI SDK

Use `search_near_vector` to run a nearest neighbor search. Because this is integrated with the Vercel AI SDK, you stream the resulting data objects directly into your frontend UI. The user sees the search results pop up live—no loading spinner needed.

Inspect Weaviate Schema for Runtime Checks

Need to know what collections exist before building a query? Call `get_full_schema` first. This gives your agent all the necessary class definitions, ensuring your subsequent calls like `get_class_schema` hit the right targets. It's crucial context you stream into the process.

Check Cluster Health with Weaviate MCP Server

Before running any heavy query, check the infrastructure status using `get_cluster_nodes`. This tells your agent if all nodes are online and operational. It’s a quick pre-flight check that guarantees your data retrieval steps work right out of the gate.

Setup guide

Set up Weaviate 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 Weaviate 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 Weaviate 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 Weaviate. 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.

Why Choose Vinkius

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Common questions about Weaviate MCP in Vercel AI SDK

You call `search_near_vector` using your query vector. The results stream into the UI immediately, letting users see exactly which data objects match their request in real time.

Absolutely. Use `get_object_details` by providing a UUID. The resulting metadata streams into your application, letting you build dynamic UIs around specific data points.

You call `get_full_schema`. This single tool retrieves every class definition from your Weaviate instance. Your agent can then use this map to guide multi-step operations.

Yes, it's designed for streaming output. This means when our MCP Server returns data—whether it’s node status or object details—the user sees it appear live in your frontend.

This server handles the metadata and content of data objects. When you run a search, the system reads the UUIDs and associated textual/vector data from those objects.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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