How to Use the NCREIF Custom Query MCP in Vercel AI SDK

Q: How do I connect NCREIF Custom Query to the Vercel AI SDK?

Use the @ai-sdk/mcp package to initialize your client. You point the transport to your Vinkius endpoint, then pass the tools directly into your generateText function.

Q: Does NCREIF Custom Query support real-time updates in Vercel AI SDK?

It does. By using streamText, you can push the output of your tools to the UI as they arrive. This makes the data feel responsive to your end users.

Stream real-time NCREIF index data directly into your React interfaces using the Vercel AI SDK and this MCP Server.

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Connect NCREIF Custom Query MCP to Vercel AI SDK

Create your Vinkius account to connect NCREIF Custom Query 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 NCREIF Custom Query with Vercel AI SDK

Ask AI about this MCP

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Live NPI performance dashboards

Feed historical index data straight into your frontend components. When your agent triggers `get_historical_npi`, the results stream into your UI without waiting for a backend refresh. This keeps your users looking at current numbers instead of staring at loading spinners. It turns raw benchmark data into immediate, interactive visuals.

Dynamic SQL execution in the browser

Run complex queries against institutional real estate datasets using `execute_query`. Your AI client sends the request, and the response flows through your Edge Functions to the client side. This removes the need for custom API endpoints for every specific data request. You get raw results delivered exactly where your application needs them.

Instant KPI retrieval

Surface predefined industry metrics using `get_predefined_kpi` inside your streaming text responses. Your interface updates the moment the agent finishes the call. This method cuts out the middleman in your data pipeline. Your users see the final performance figures as they appear in the source database.

Setup guide

Set up NCREIF Custom Query 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 NCREIF Custom Query 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 NCREIF Custom Query 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 NCREIF. 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 NCREIF Custom Query MCP in Vercel AI SDK

Use the `@ai-sdk/mcp` package to initialize your client. You point the transport to your Vinkius endpoint, then pass the tools directly into your `generateText` function.

Yes. Since this MCP Server uses standard HTTP transports, your Edge Functions handle the requests without performance issues. It works just like any other external data fetch.

It does. By using `streamText`, you can push the output of your tools to the UI as they arrive. This makes the data feel responsive to your end users.

The server only touches raw index data provided by the source API. No sensitive information or proprietary valuation models are saved on the Vinkius infrastructure.

Every request requires an endpoint token. Your AI client handles the authentication headers, ensuring that only your authorized agents access the data.

Use it with your favorite AI tools

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