How to Use the FlowiseAI MCP in Vercel AI SDK

Q: How do I list variables from FlowiseAI in Vercel AI SDK?

Call the listflowvariables tool. It returns an array of current global variables you can display in your admin console.

Connect FlowiseAI to Vercel AI SDK to stream your visual LLM orchestration results directly into your React or Next.js frontend.

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Connect FlowiseAI MCP to Vercel AI SDK

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

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Real-time chatflow inference

Trigger your visual pipelines directly from the browser by calling `execute_chatflow_prediction` within your streaming response. This lets your frontend react to the flow output as it arrives. You avoid the typical backend bottlenecks by pushing the logic through the Vercel AI SDK runtime. The result is a UI that updates instantly based on your defined chatflow logic.

Manage vector data on the fly

Keep your RAG pipelines fresh by calling `upsert_vector_data` whenever your user submits new content. It pushes data straight to your configured vector store without extra middleware. Your app handles state updates while the MCP server manages the heavy lifting of the database writes. This tight loop keeps your search context accurate without manual syncing.

Introspection for your UI

Query your available chatflows using `list_chatflows` to dynamically populate dropdowns or settings panels in your interface. You can show users exactly what flows are available at any given moment. Combine this with `get_chatflow_details` to pull specific configuration metadata. Your app stays in sync with your FlowiseAI workspace automatically.

Setup guide

Set up FlowiseAI 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 FlowiseAI 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 FlowiseAI transactions",
});

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FlowiseAI. 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 FlowiseAI MCP in Vercel AI SDK

Install the MCP client package and configure your transport to point at your server instance. Use the generated tools within your streamText or generateText calls.

Yes, but you should handle the streaming state in your UI. The MCP server will return the full prediction once the FlowiseAI engine completes the task.

The server uses your provided credentials to authenticate requests. Your data stays between the MCP server and your FlowiseAI instance.

Call the `list_flow_variables` tool. It returns an array of current global variables you can display in your admin console.

The tool will throw an error that you can catch in your catch-block. You should log the error and notify the user via your frontend alert system.

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