How to Use the Mode (Collaborative Data Platform) MCP in Vercel AI SDK

Q: How do I query Mode (Collaborative Data Platform) reports in Vercel AI SDK?

You instantiate the client and expose searchreports to your agent. When the user asks for insights, the tool fetches the relevant metadata and streams it into your response.

Q: Can Vercel AI SDK manage Mode (Collaborative Data Platform) workspace members?

Yes, you use listmembers to pull the active user list. It allows you to build interfaces that filter reports based on the owner's identity.

Q: Does Mode (Collaborative Data Platform) integrate with Vercel AI SDK for space management?

It does. You call listspaces to retrieve the current collection hierarchy and display it in your dashboard.

Stream Mode (Collaborative Data Platform) data directly into your Vercel AI SDK frontends for real-time analytical visibility.

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Connect Mode (Collaborative Data Platform) MCP to Vercel AI SDK

Create your Vinkius account to connect Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) with Vercel AI SDK

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Real-time report streaming for AI SDK

Feed your UI live analytical data without waiting for long-running processes. By invoking `get_report` and `search_reports` through your edge functions, you push report metadata directly to the user’s screen as it arrives. This bypasses the typical loading spinner experience. You get a fluid interface where the data feels like part of the application state instead of a remote fetch.

Direct workspace auditing via AI SDK

Map out your organizational structure by calling `list_spaces` and `get_space` within your request handlers. This gives your frontend the context needed to render navigation for complex data environments. Your app stays in sync with the workspace without extra middleware. You simply pass the tool results into your stream and let the UI handle the presentation.

Connector visibility for AI SDK

Expose exactly which data warehouses are linked to your environment using `list_data_sources`. This lets your users verify their data context before running heavy queries. It keeps the user informed about the source of truth. By showing the active connections, you prevent misinterpretation of analytical outputs.

Setup guide

Set up Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) transactions",
});

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

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Common questions about Mode (Collaborative Data Platform) MCP in Vercel AI SDK

You instantiate the client and expose `search_reports` to your agent. When the user asks for insights, the tool fetches the relevant metadata and streams it into your response.

Yes, you use `list_members` to pull the active user list. It allows you to build interfaces that filter reports based on the owner's identity.

It does. You call `list_spaces` to retrieve the current collection hierarchy and display it in your dashboard.

The server uses an endpoint token to handle auth. All data requests are scoped to your provided credentials, keeping your analytics private.

The server only touches report metadata and workspace configuration. It never exposes raw, sensitive database records, ensuring your analytical assets remain protected.

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