How to Use the Activepieces MCP in Vercel AI SDK

Q: Can Vercel AI SDK read Activepieces connection secrets?

Your agent can list available integrations using listappconnections but it cannot extract raw OAuth tokens or passwords. The system only returns metadata about what is currently configured. This keeps your credentials secure while still allowing the AI to know which services are available.

Q: How does Activepieces handle data privacy for flow runs?

The getflowrun tool exposes execution logs, which might contain customer emails or internal IDs depending on your automation. Vinkius isolates this data in a V8 sandbox that dies immediately after the request finishes. Nothing persists on our infrastructure, ensuring your sensitive pipeline payloads remain private.

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Connect Activepieces MCP to Vercel AI SDK

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

Ask AI about this MCP

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Claude

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Control automations via Vercel AI SDK

This server gives your agent direct access to `create_flow` and `apply_flow_operation` commands. Building a custom UI for internal operations usually means writing dozens of API endpoints. Now you just wire up the MCP transport and let the language model handle the logic. Users ask your Next.js app to set up a new integration. The agent immediately calls `list_app_connections` to check existing credentials before creating anything. Because the AI SDK streams tool results, the user sees the new workflow configure itself live on screen instead of staring at a loading spinner.

Stream execution data to the UI

Fetching execution logs with `get_flow_run` pulls raw diagnostic data directly into your frontend components. Support teams need to know exactly why an automation failed without logging into another dashboard. Your chat interface becomes the primary window into system health. When someone types a query about recent errors, the agent runs `list_flow_runs` to grab the history. It parses the payload and feeds formatted text back to the browser. You get a real-time debugging console built entirely on natural language.

Manage internal tables dynamically

Interacting with custom platform data requires the `list_tables` and `list_records` tools. Developers often ignore built-in storage because the API overhead slows down feature delivery. Exposing these commands through an MCP Server changes the math completely. Your agent can read existing configuration rows and immediately trigger an `update_record` call based on user input. We see teams building entire CRM interfaces in Svelte using just these basic primitives. The database updates happen instantly while the chat response is still generating.

Setup guide

Set up Activepieces 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 Activepieces 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 Activepieces 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 Activepieces. 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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Real-time monitoring

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Activepieces MCP in Vercel AI SDK

Install the @ai-sdk/mcp package and create an HTTP transport pointing to your Vinkius endpoint URL. Pass the resulting tool list into streamText or generateText. Remember to call mcpClient.close() when the session ends.

Your agent can list available integrations using list_app_connections but it cannot extract raw OAuth tokens or passwords. The system only returns metadata about what is currently configured. This keeps your credentials secure while still allowing the AI to know which services are available.

Standard APIs require you to write parsing logic, error handling, and UI updates for every endpoint. An MCP Server provides a standardized interface that language models already understand. You write zero glue code to get full platform control.

Yes, the HTTP transport method is fully compatible with edge runtimes. You can trigger tools from this MCP Server in a lightweight serverless environment. Latency stays low because the connection only opens when a tool executes.

The get_flow_run tool exposes execution logs, which might contain customer emails or internal IDs depending on your automation. Vinkius isolates this data in a V8 sandbox that dies immediately after the request finishes. Nothing persists on our infrastructure, ensuring your sensitive pipeline payloads remain private.

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