How to Use the EBI InterPro MCP in Vercel AI SDK

Stream real-time protein annotations directly into your Vercel AI SDK interface without waiting for background batch processes.

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Connect EBI InterPro MCP to Vercel AI SDK

Create your Vinkius account to connect EBI InterPro 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 EBI InterPro with Vercel AI SDK

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Live protein classification in Vercel AI SDK

Feed protein sequence data into your front-end components using `search_proteins` and `get_protein_entries`. The data streams straight to the browser, letting your users see domain assignments as they arrive. Stop relying on static reports. By using `get_entry` inside your React or Next.js app, you render functional domain metadata the second your agent identifies a sequence.

Visualizing taxonomy with Vercel AI SDK

Map evolutionary relationships by piping `get_entry_taxonomy` output into your UI charts. Your users get immediate visual feedback on how a specific domain is distributed across species. This workflow bypasses standard server-side bottlenecks. You trigger `get_taxonomy` directly from your client-side logic to populate your interface with accurate, up-to-the-second biological data.

Structural data access in Vercel AI SDK

Query PDB structures using `get_entry_structures` and `get_structure` to provide deep context for your protein analysis tools. Your Vercel AI SDK agents retrieve 3D experimental data without extra middleware. Building an interactive viewer becomes straightforward. You call the tools, pass the results to your frontend state, and the user sees the structure details immediately.

Setup guide

Set up EBI InterPro 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 EBI InterPro 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 EBI InterPro transactions",
});

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

Get your connection token →

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Why Choose Vinkius

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Common questions about EBI InterPro MCP in Vercel AI SDK

You instantiate the client and expose the tools to your agent. Once connected, your agent calls the EBI InterPro MCP Server to fetch data directly during the generation cycle.

Yes, but keep an eye on your request volume. You should implement your own throttling if you plan to hit the EBI InterPro endpoints with thousands of simultaneous queries.

Vinkius handles the connection tunnel securely. Your sequences stay within the request flow, and we don't store your biological data on our servers.

It does. Because the server is MCP-compliant, your agent receives the data chunks as they come back from the database, which you then stream to the client.

The server returns a structured error. You handle this in your agent logic just like any other tool output, allowing for graceful retries or user notifications.

Use it with your favorite AI tools

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