How to Use the EBI Proteins API MCP in Vercel AI SDK

Stream UniProt data directly into your Vercel AI SDK interface for real-time protein analysis and sequence retrieval.

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Connect EBI Proteins API MCP to Vercel AI SDK

Create your Vinkius account to connect EBI Proteins API 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 Proteins API with Vercel AI SDK

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Live protein data in your React UI

Feed protein sequences and annotations directly into your frontend components. Use `get_protein` to pull UniProt data that streams into your UI as the user types. Your interface updates the moment the data arrives. No more waiting for static loading states when you can render structural features from `get_protein_features` instantly.

Search and filter proteomes dynamically

Trigger `search_proteins` or `search_proteomes` based on live user input. This MCP Server handles the lookup while your application displays the results in real-time. Your users get immediate feedback when searching by gene name or keyword. The tool maps the UniProt response directly into your state management layer.

Map genomic coordinates to sequences

Connect protein data to genomic maps by invoking `get_coordinates`. This provides the exact chromosome and translation IDs needed for clinical research dashboards. Your app handles the mapping logic while the server manages the heavy lifting of the EBI backend. It's built for low-latency lookups within your edge functions.

Setup guide

Set up EBI Proteins API 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 Proteins API 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 Proteins API transactions",
});

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

Get your connection token →

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Common questions about EBI Proteins API MCP in Vercel AI SDK

Install the MCP client package and initialize the connection to this server. Once connected, pass the tools into your generateText call to start querying.

Yes, it is designed for streaming. The tool outputs are consumed directly by the SDK, allowing your UI to render the protein annotations as they arrive.

You can, provided you use the HTTP transport for the MCP server. This keeps your edge functions lightweight while offloading the data retrieval.

This server uses a single endpoint token. Pass this token during the client initialization to authorize your requests to the EBI backend.

The server only touches public UniProt accession data. It does not store or process your private patient identifiers, ensuring your clinical data stays local.

Use it with your favorite AI tools

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

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