How to Use the LlamaIndex (AI Data Framework & RAG) MCP in Vercel AI SDK

Q: Can I list my active projects in LlamaIndex (AI Data Framework & RAG) using Vercel AI SDK?

Yes, just call listprojects via the connected client. It returns your current project list so you can map them to UI selectors in your React components.

Q: Is it possible to track indexed files in LlamaIndex (AI Data Framework & RAG) via Vercel AI SDK?

Absolutely. Use listfiles to retrieve the list of source documents currently processed by the target pipeline.

Stream real-time RAG data directly into your frontend with LlamaIndex (AI Data Framework & RAG) and Vercel AI SDK.

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Connect LlamaIndex (AI Data Framework & RAG) MCP to Vercel AI SDK

Create your Vinkius account to connect LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) with Vercel AI SDK

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Live pipeline data streaming

Feed your UI with live insights by calling `query_pipeline` directly through your edge functions. Your AI client receives the data as it processes, keeping your interface responsive. Stop waiting for massive payloads. The MCP Server pushes results straight into your `streamText` response, ensuring your users see the retrieval happen in real-time.

Audit your RAG source files

Verify exactly what content your AI is seeing by using `list_files` to inspect the raw ingestion state. This keeps your search results transparent and debuggable. When you need to know why a specific answer appeared, check the metadata returned by these calls. It gives you a clear window into how your pipeline handles incoming documents.

Configure pipelines on the fly

Use `list_projects` and `list_pipelines` to dynamically switch between different RAG setups within your application. You maintain control over which data index your AI client uses for a given request. Managing your backend state becomes a standard function call. You toggle between environments or specific projects without needing to redeploy your frontend code.

Setup guide

Set up LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) 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 LlamaIndex (AI Data Framework & RAG) transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LlamaIndex. 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 LlamaIndex (AI Data Framework & RAG) MCP in Vercel AI SDK

You initialize the client, fetch the tool definitions, and pass them to your `streamText` function. This allows the model to trigger `query_pipeline` whenever it needs contextual data for your users.

Yes, just call `list_projects` via the connected client. It returns your current project list so you can map them to UI selectors in your React components.

Absolutely. Use `list_files` to retrieve the list of source documents currently processed by the target pipeline.

Use the built-in auth provider or pass your endpoint token during client initialization. Your agent handles the handshake securely before any data flows.

Your documents remain isolated within your private LlamaCloud environment. The MCP server only acts as a bridge, and your AI client never stores the raw data locally.

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