How to Use the Liaison MCP in Vercel AI SDK

Q: How do I set up the Liaison MCP Server with the Vercel AI SDK?

Install @ai-sdk/mcp and create a client pointing to your Vinkius endpoint URL. Pass the tools from mcpClient.tools() into streamText to let your agent call Liaison endpoints directly. Always close the client connection when the request finishes.

Q: How do I handle batch exports in conversational interfaces?

Your client uses listbatches to show the user a list of available groups, then pulls the specific details using getbatch. This lets users query large chunks of admissions data through a simple chat interface.

Feed real-time admissions data directly into your Next.js interface using the Vercel AI SDK.

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Connect Liaison MCP to Vercel AI SDK

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Setup Liaison with Vercel AI SDK

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Live applicant tracking in your Vercel AI SDK app

Stop making users stare at loading spinners while waiting for admissions data. This Vercel AI SDK integration lets your interface render applicant details as they stream in from the server. Your users see live status updates the second the backend fetches them. This MCP Server lets your UI call `list_applications` and stream the results straight to Vercel AI SDK components. When a user clicks an applicant, `get_application` instantly pulls the specific academic record they need to review.

Instant program and designation mapping

Academic programs change, and manual mapping in Next.js is a nightmare. This Vercel AI SDK setup lets your interface resolve program requirements on the fly. You query active offerings without building custom database sync scripts. Your Vercel AI SDK client uses `list_programs` and `list_designations` to show applicants the exact tracks available. It grabs current admissions options directly from the Liaison API, so your front end never displays stale academic codes.

Direct custom field rendering

Every school has custom questions that break standard Vercel AI SDK forms. This toolset lets your UI adapt to whatever custom fields the admissions team configures on the backend. By calling `list_custom_fields`, your Vercel AI SDK system renders dynamic form inputs matching the exact schema. It means your React forms match the backend admissions database without a redeploy.

Setup guide

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

Install `@ai-sdk/mcp` and create a client pointing to your Vinkius endpoint URL. Pass the tools from `mcpClient.tools()` into `streamText` to let your agent call Liaison endpoints directly. Always close the client connection when the request finishes.

Yes, that is the main reason to use this setup. Your Vercel AI SDK client calls `get_application` to pull the raw applicant data and streams the parsed details directly into your UI components without blocking the page render.

Absolutely. The Vercel AI SDK is built for edge runtimes, and the Vinkius MCP Server handles the heavy API calls, keeping your edge functions light and fast. You get low latency without managing massive dependency bundles.

Your client uses `list_batches` to show the user a list of available groups, then pulls the specific details using `get_batch`. This lets users query large chunks of admissions data through a simple chat interface.

Your applicant files and academic records never touch third-party databases during transit. The server runs in a secure, isolated V8 sandbox on Vinkius, passing data directly from the admissions API to your local client over an encrypted tunnel.

Use it with your favorite AI tools

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