How to Use the Wrike MCP in Vercel AI SDK

Q: How can I use Wrike with the Vercel AI SDK?

You call mcpClient.tools() to expose the specific Wrike functions your app needs. Your agent then calls these tools, and the results stream right into your React component for immediate display.

Stream AI-powered project insights into your React frontends with the Vercel AI SDK.

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…and any MCP-compatible client

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Connect Wrike MCP to Vercel AI SDK

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

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Claude

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Build live task status feeds.

Use `list_tasks` to pull a list of tasks, filtering by statuses like 'Active' or 'Completed'. Your users see this data stream in real-time on the page, not after a slow loading spinner. You can also call `get_task_details` when a user clicks into a specific item. The Vercel AI SDK streams that full task info directly to the UI, making the whole interaction feel instant.

Monitor team communication threads.

Need to know what was discussed? `list_task_comments` pulls the entire discussion history for any given project. Your client can display these messages appearing instantly as they are fetched, which is critical for a good user experience. Developers use this streamability to build better UIs around Wrike data. It's perfect when showing real-time updates in an Edge Function.

Manage project structure visibility.

The `list_folders_and_projects` tool lets your frontend show the full hierarchy of a workspace. This means users see every associated folder and project right when they load the page. It’s simple to display this tree view data as it streams, ensuring that even deep structures load without visible lag or chunking.

Setup guide

Set up Wrike 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 Wrike 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 Wrike 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 Wrike. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Common questions about Wrike MCP in Vercel AI SDK

You call `mcpClient.tools()` to expose the specific Wrike functions your app needs. Your agent then calls these tools, and the results stream right into your React component for immediate display.

Yes, it's built for that. The whole point is streaming: you see data appearing live in the UI, whether you are pulling comments via `list_task_comments` or getting task details.

The setup supports an authentication provider for OAuth. This ensures that your app only accesses data associated with the logged-in end user, keeping things secure.

Since it's designed for Edge Function compatibility, you get high reliability. The architecture is meant to handle real-time data flow, so performance isn't a concern.

This server touches task discussion data (comments and attachments), user profiles (`get_user_profile`), and the project hierarchy (`list_folders_and_projects`).

Use it with your favorite AI tools

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