How to Use the Flow MCP in Vercel AI SDK

Connect Flow to Vercel AI SDK to stream task management data directly into your React and Next.js interfaces in real-time.

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Connect Flow MCP to Vercel AI SDK

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

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Real-time task synchronization

Feed your UI live updates using `list_tasks` and `list_projects`. Your users see the state of their workspace change as the AI processes requests, removing the need for static loading states. This MCP Server pushes data through the SDK transport layer. You get granular control over how task metadata displays, ensuring the information stays relevant to the user's current view.

Automated project reporting

Trigger `get_project` and `list_task_comments` to pull deep insights into your frontend dashboard. The AI client processes these results to generate summaries without forcing the user to leave the page. Build dynamic views that react to changes in your workflow. By piping tool output directly into your components, you bridge the gap between backend task states and frontend visibility.

Direct task manipulation

Allow your users to modify active work using `create_task` and `update_task` directly from your application interface. The AI client handles the logic while the server ensures the data hits your workspace correctly. Everything happens via the SDK connection. You define the triggers, and the server executes the mutation, providing immediate feedback loop closure for the end user.

Setup guide

Set up Flow 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 Flow 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 Flow transactions",
});

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

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Common questions about Flow MCP in Vercel AI SDK

You map the tool outputs from this MCP server into your UI components. The SDK handles the streaming, so you just need to render the data as it arrives.

Yes, it works fine with Edge Functions. You just need to ensure your environment variables for the endpoint are configured correctly.

Your data stays private. The server uses an endpoint token for authentication, and we never store your project credentials on our side.

You can. Just call the tool through the client and pass the result to your state management logic.

It only touches your project metadata, task descriptions, and internal comments. We keep the connection locked to these specific fields.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.