How to Use the Toggl Plan MCP in Vercel AI SDK

Q: How do I use Toggl Plan with Vercel AI SDK to check available users?

You call listworkspaceusers and it returns a list of everyone who has access to that workspace. This is key for making sure your agent doesn't try to assign tasks to someone who shouldn't be there.

Q: Can I use Toggl Plan with Vercel AI SDK to add new tasks?

Yep. You run the createtimelinetask tool, giving it the workspace ID, a task name, and the project ID. The timeline updates right away, which is perfect for real-time UI feedback.

Q: What data types does Toggl Plan touch when integrated with Vercel AI SDK?

This MCP server handles various data points, including workspace IDs, user lists (listworkspaceusers), and structured task objects (the JSON payload for updatetimelinetask). You're dealing with core project metadata.

Q: How do I list all available project tags using Toggl Plan and Vercel AI SDK?

You call listworkspacetags, passing in the workspace ID. This gives you a full inventory of every tag used for categorization, helping your agent keep everything organized.

Visualize project timelines in real time with the Vercel AI SDK.

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Connect Toggl Plan MCP to Vercel AI SDK

Create your Vinkius account to connect Toggl Plan 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 Toggl Plan with Vercel AI SDK

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View and Manage Workloads via MCP Server

The agent lists every task assigned to a workspace using `list_timeline_tasks` (which needs the workspace ID). You can then see which projects are active by calling `list_workspace_projects`. It's pretty straightforward; you just need that one workspace ID to pull everything together.

Update Project Tasks with Vercel AI SDK

Need to change a project phase? You send an update object via `update_timeline_task` and the timeline adjusts instantly. This tool takes structured JSON, so you control exactly which fields get modified. Don't forget that if something goes wrong, you can always check the status using `get_task_details`.

Build Project Structure with Vercel AI SDK

The server helps map out your entire project structure. You pull a list of all major milestones through `list_milestones`, or you can get a deep dive into one specific area using `get_project_details`. Building the initial task requires calling `create_timeline_task` with the necessary workspace, name, and project ID.

Setup guide

Set up Toggl Plan 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 Toggl Plan 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 Toggl Plan transactions",
});

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

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Common questions about Toggl Plan MCP in Vercel AI SDK

You call `list_workspace_users` and it returns a list of everyone who has access to that workspace. This is key for making sure your agent doesn't try to assign tasks to someone who shouldn't be there.

Yep. You run the `create_timeline_task` tool, giving it the workspace ID, a task name, and the project ID. The timeline updates right away, which is perfect for real-time UI feedback.

This MCP server handles various data points, including `workspace IDs`, user lists (`list_workspace_users`), and structured task objects (the JSON payload for `update_timeline_task`). You're dealing with core project metadata.

Absolutely. Since the results stream directly into your React or Next.js UI, you see the data appear live—no loading spinners required. It's built for that real-time developer experience.

You call `list_workspace_tags`, passing in the workspace ID. This gives you a full inventory of every tag used for categorization, helping your agent keep everything organized.

Use it with your favorite AI tools

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