How to Use the Lindy (Autonomous AI Employees) MCP in Vercel AI SDK

Q: How do I audit Lindy (Autonomous AI Employees) runs inside my Vercel AI SDK app?

Use the listruns tool to pull recent execution history. You can then pipe those IDs into getrun to inspect the specific state of any task.

Q: Can I stop a Lindy (Autonomous AI Employees) process from my Vercel AI SDK interface?

Yes. Call cancelrun with the specific run ID. This triggers a hard stop on the server side, ensuring the agent halts immediately.

Q: How do I list authorized connections for Lindy (Autonomous AI Employees) in Vercel AI SDK?

The listintegrations tool returns a JSON array of all connected third-party apps. You can map these to your UI to show users exactly what data sources the agent accesses.

Q: Is it possible to trigger tasks based on user input?

You can map user inputs to a JSON payload and send it to triggerlindy. This allows your interface to act as a command center for your autonomous agents.

Trigger autonomous work directly from your Vercel AI SDK interface. Monitor reasoning logs and manage Lindy runs in real-time.

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Connect Lindy (Autonomous AI Employees) MCP to Vercel AI SDK

Create your Vinkius account to connect Lindy (Autonomous AI Employees) 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 Lindy (Autonomous AI Employees) with Vercel AI SDK

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Real-time execution monitoring for Vercel AI SDK

Stream your agent's reasoning logs straight into your UI using `get_run_logs`. Stop waiting for loading states and show your users exactly what the model is thinking as it processes your requests. Connect your frontend to `get_run` to display live state updates. You'll see the status of external API calls and human-in-the-loop blocks the moment they occur.

Trigger tasks without leaving your Next.js app

Initiate complex operations by firing `trigger_lindy` from your serverless functions. Pass your JSON payloads directly from the client to kick off background processes without managing separate infrastructure. Keep track of your active agent fleet using `list_lindies`. You can pull the full configuration mapping, including prompts and tools, to build dynamic dashboards that reflect your current autonomous architecture.

Secure integration management for your AI client

Audit your linked third-party connections like Gmail or Slack using `list_integrations`. This gives your Vercel AI SDK frontend a clear view of what your agents can touch and where they send data. Control runaway processes with `cancel_run` directly from your event handlers. If a task hits an infinite loop, you can kill the execution path immediately, keeping your API spend and operational logic under your thumb.

Setup guide

Set up Lindy (Autonomous AI Employees) 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 Lindy (Autonomous AI Employees) 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 Lindy (Autonomous AI Employees) 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 Lindy. 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 Lindy (Autonomous AI Employees) MCP in Vercel AI SDK

Use the `list_runs` tool to pull recent execution history. You can then pipe those IDs into `get_run` to inspect the specific state of any task.

Yes. Call `cancel_run` with the specific run ID. This triggers a hard stop on the server side, ensuring the agent halts immediately.

The `list_integrations` tool returns a JSON array of all connected third-party apps. You can map these to your UI to show users exactly what data sources the agent accesses.

Your data stays within the defined Lindy workspace. The MCP server only acts as a transport layer for tool calls, not a storage repository for your private information.

You can map user inputs to a JSON payload and send it to `trigger_lindy`. This allows your interface to act as a command center for your autonomous agents.

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