How to Use the Gradient AI (LLM API & Finetuning) MCP in Vercel AI SDK

Q: How are files handled when using Gradient AI (LLM API & Finetuning) with Vercel AI SDK?

You use the uploadfile tool to send binary data or text to the server. The server processes the file and returns an ID you use for training or RAG.

Q: Does Gradient AI (LLM API & Finetuning) keep my Vercel AI SDK data private?

All data uploaded via uploadfile or processed in a RAG collection stays within your isolated environment. The server enforces strict access controls for every request.

Stream fine-tuned model completions and RAG results directly into your Vercel AI SDK frontends for real-time user experiences.

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Connect Gradient AI (LLM API & Finetuning) MCP to Vercel AI SDK

Create your Vinkius account to connect Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) with Vercel AI SDK

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Real-time model completions in Vercel AI SDK

Pipe live model outputs directly into your React or Next.js components. When your app calls `complete_model`, the data flows through the stream without waiting for a full response cycle. Your users watch the text render as it hits the wire. It removes the laggy feel of traditional API calls by integrating the Gradient AI (LLM API & Finetuning) toolset into your edge-compatible runtime.

Dynamic RAG collections for Vercel AI SDK

Build interactive search UIs using `create_rag_collection` and `answer_question`. You can index documentation or user data on the fly while maintaining strict control over the context window. Your frontend handles the heavy lifting of displaying retrieved snippets. This MCP server acts as the data backbone, ensuring your interface reflects the exact state of your vector collections.

Automated fine-tuning pipelines

Trigger training jobs directly from your application logic using `upload_file` and `fine_tune_model`. You don't need a separate dashboard to manage your custom model lifecycle. Everything happens via the server-side SDK. You can create specialized models for specific user personas and monitor their status with `get_model` without leaving your codebase.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) transactions",
});

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

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Why Choose Vinkius

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

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Common questions about Gradient AI (LLM API & Finetuning) MCP in Vercel AI SDK

Install the MCP client packages and point your transport to the server URL. You then pass the tool definitions into your `generateText` call to start interacting with the models.

Yes. Since the server uses the standard MCP protocol, the SDK handles the streaming chunks automatically. You just map the tool output to your UI state.

Yes, provided your transport configuration remains lightweight. The server handles the logic, keeping your edge functions lean and fast.

You use the `upload_file` tool to send binary data or text to the server. The server processes the file and returns an ID you use for training or RAG.

All data uploaded via `upload_file` or processed in a RAG collection stays within your isolated environment. The server enforces strict access controls for every request.

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