How to Use the VectorShift (AI Workflow & RAG Automation) MCP in Vercel AI SDK

Q: Can I pause a workflow using VectorShift (AI Workflow & RAG Automation) with Vercel AI SDK?

Yep, you can use pausepipeline to temporarily halt a running process. This is useful if your UI needs to show an 'on hold' state while waiting for user input or approval.

Q: How do I list all available chatbots using VectorShift (AI Workflow & RAG Automation) and Vercel AI SDK?

You call listchatbots on the MCP Server. This returns a list of every chatbot instance that's currently configured in your environment, letting you know what services are ready to stream data.

Q: If I need to delete content, what tool do I use with VectorShift (AI Workflow & RAG Automation) and Vercel AI SDK?

To clean up, you'll use the deletion tools. For example, deleteknowledgebase removes an entire knowledge base container managed by the MCP Server.

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Connect VectorShift (AI Workflow & RAG Automation) MCP to Vercel AI SDK

Create your Vinkius account to connect VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) with Vercel AI SDK

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ChatGPT

Claude

Perplexity

Show streaming chatbot responses with the MCP Server

Use `run_chatbot` to send a message and stream the response directly into your frontend. Since this is an MCP Server, you get real-time data that avoids loading spinners. This capability lets you build user-facing products where the AI interaction feels instant. The client sees the answer appear piece by piece as it's generated.

Query knowledge bases in real time using Vercel AI SDK

The `query_knowledge_base` tool lets you perform semantic searches against your indexed data. You pass a query, and the MCP Server returns relevant snippets fast. It's perfect for building custom help centers or documentation viewers that feel snappy and live. The result streams right into the user interface.

Manage AI workflows with the MCP Server

You can manage complex tasks using `create_pipeline` and then trigger them with `run_pipeline`. This structure keeps your backend clean while giving users a clear workflow. If you need to show the user that a multi-step process is running, this server handles it. You're not just calling an API; you're running a visible, managed operation.

Setup guide

Set up VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) 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 VectorShift. 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 VectorShift (AI Workflow & RAG Automation) MCP in Vercel AI SDK

VectorShift provides the backend logic for your workflows. You connect to it via the MCP Server, and your Vercel AI SDK client streams the results from that server directly into your React component.

The MCP Server manages knowledge base documents, which can come from uploaded files or external URLs. This is the specific type of data your client will index and query.

Yep, you can use `pause_pipeline` to temporarily halt a running process. This is useful if your UI needs to show an 'on hold' state while waiting for user input or approval.

You call `list_chatbots` on the MCP Server. This returns a list of every chatbot instance that's currently configured in your environment, letting you know what services are ready to stream data.

To clean up, you'll use the deletion tools. For example, `delete_knowledge_base` removes an entire knowledge base container managed by the MCP Server.

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