How to Use the CERN Open Data MCP in Vercel AI SDK

Q: How do I stream CERN Open Data record metadata using the Vercel AI SDK?

You configure the MCP client using createMCPClient and pass the tools directly to streamText. When the user asks for a dataset, the SDK streams the metadata from getrecord directly into your UI components. This keeps your application responsive even when processing large JSON payloads.

Render real-time collision events and LHC metadata directly into your React components using the Vercel AI SDK and this MCP Server.

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Connect CERN Open Data MCP to Vercel AI SDK

Create your Vinkius account to connect CERN Open Data 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 CERN Open Data with Vercel AI SDK

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Live-stream LHC statistics with the Vercel AI SDK

Your frontend doesn't need to hang while querying massive scientific databases. By combining the Vercel AI SDK with this MCP Server, your UI streams live statistics from `get_portal_statistics` directly into your client-side charts. Users watch real-time event counts and dataset distributions load chunk-by-chunk instead of staring at a blank screen. This setup bypasses slow API gateways by running directly on edge functions. You can instantly render the distributions of CMS or DELPHI datasets using `list_experiments` to let researchers filter through petabytes of collision data without leaving your application's main dashboard.

Interactive particle physics glossary components

Build educational frontends that explain complex physics terms on hover. When your agent encounters complex terms in a dataset, it calls `get_glossary` to pull definitions for variables like pseudorapidity or transverse momentum. The Vercel AI SDK handles the state, rendering clean definitions inside your React tool-call components. You can use `search_documentation` alongside this glossary to build interactive guides. Instead of forcing students to read dry PDFs, your UI renders step-by-step guides on how to process raw ROOT files, fetching only the specific paragraphs they need.

Real-time ROOT file registry explorer

Give researchers a way to browse massive datasets without writing custom terminal scripts. Your React frontend can use `list_record_files` to display file sizes, checksums, and EOS URIs as soon as the user selects a record. The Vercel AI SDK streams these file lists instantly, preventing browser timeouts on records that contain thousands of files. Users can resolve DOIs on the fly using `get_record_by_doi` to pull up specific CMS or ATLAS datasets. The interface updates instantly, displaying direct download links and event counts without requiring a full page reload.

Setup guide

Set up CERN Open Data 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 CERN Open Data 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 CERN Open Data 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 CERN Open Data. 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 CERN Open Data MCP in Vercel AI SDK

You configure the MCP client using `createMCPClient` and pass the tools directly to `streamText`. When the user asks for a dataset, the SDK streams the metadata from `get_record` directly into your UI components. This keeps your application responsive even when processing large JSON payloads.

Yes, this setup is built to run on the edge. The Vercel AI SDK calls the server over lightweight HTTP transports, allowing your edge functions to resolve particle physics categories using `list_categories` with minimal latency.

The SDK uses streaming text and tool call generation to prevent gateway timeouts. If your agent is running a wide search like `search_datasets` for Higgs boson data, the partial tool calls are sent to the client immediately, showing the user exactly what query is running.

Use the Vercel AI SDK's helper hooks to track the status of `search_by_collision_energy`. Your UI can dynamically render custom search filters for 13TeV or 8TeV datasets as the agent returns matching records.

All requests to this MCP Server run inside an ephemeral, zero-trust Vinkius sandbox. Your query parameters, like specific DOIs or physics categories, never persist on any server, keeping your research queries private.

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