How to Use the FutureVault MCP in Vercel AI SDK

Q: How do I configure FutureVault with Vercel AI SDK?

Install @ai-sdk/mcp and create an HTTP client pointing to your Vinkius endpoint. Pass the tools from mcpClient.tools() into streamText to let your agent call tools like listdigitalvaults.

Q: How do I handle file searches in Next.js?

You pass searchvaultcontent to your agent tools array. The agent searches your vaults and streams the matching document list back to your client-side search bar.

Stream FutureVault file structures and metadata directly into your React or Next.js UI using the Vercel AI SDK.

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Connect FutureVault MCP to Vercel AI SDK

Create your Vinkius account to connect FutureVault 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 FutureVault with Vercel AI SDK

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Render real-time folder trees with Vercel AI SDK

`list_vault_folders` fetches the folder structure of any digital vault so your frontend can draw the UI tree as the user watches. Your agent calls this tool and streams the raw directory nodes straight into the Next.js edge runtime without waiting for a full page reload. Once the folders load, the agent triggers `list_folder_documents` to populate those directories with file names. The Vercel AI SDK handles this partial data stream, letting your React components render individual files instantly instead of showing a blank loading spinner.

Stream document metadata directly into UI components

`get_document_metadata` pulls specific file details like retention dates, file size, and uploaders. When a user hovers over a file in your custom portal, the agent runs this tool to fetch the payload instantly. Because the Vercel AI SDK supports edge-compatible tool streaming, this metadata populates your custom cards in real-time. You don't have to build complex state machines to handle loading states for individual file attributes.

Validate MCP Server connections on the edge

`verify_api_connection` checks if your FutureVault API credentials are still valid before running complex document queries. Your agent runs this diagnostic tool at the start of a session to confirm the connection is live. If the connection fails, the Vercel AI SDK streams the error state directly to the user's browser. This keeps your edge functions fast and prevents broken API tokens from locking up your Next.js application using this MCP Server.

Setup guide

Set up FutureVault 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 FutureVault 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 FutureVault 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 FutureVault. 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 FutureVault MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and create an HTTP client pointing to your Vinkius endpoint. Pass the tools from `mcpClient.tools()` into `streamText` to let your agent call tools like `list_digital_vaults`.

Yes, you can use the Vercel AI SDK to stream tool calls. When the agent triggers `get_document_metadata`, the SDK sends the tool results directly to your frontend, allowing you to render the document details immediately.

Absolutely. The server runs in an isolated V8 sandbox on Vinkius, and the Vercel AI SDK connects via standard HTTP, making it fully compatible with edge runtimes.

You pass `search_vault_content` to your agent tools array. The agent searches your vaults and streams the matching document list back to your client-side search bar.

All document metadata and folder structures are processed in ephemeral, zero-trust V8 isolates. The SDK only receives the specific JSON outputs from tools like `get_document_metadata`, ensuring your raw financial files and trust documents are never cached or exposed to external servers.

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