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

Q: What if I need to stop a process? What tools should I use with VectorShift (AI Workflow & RAG Automation) and Mastra AI?

You have terminatepipeline for stopping an active workflow, or you can use deletechatbot if the conversational agent is no longer needed.

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

You'll use specific deletion methods. For instance, deleteknowledgebasedocuments lets you scrub specific files by ID without deleting the whole knowledge base.

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Connect VectorShift (AI Workflow & RAG Automation) MCP to Mastra AI

Create your Vinkius account to connect VectorShift (AI Workflow & RAG Automation) to Mastra AI 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 Mastra AI

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Perplexity

Manage complex pipelines for Mastra AI

The `create_pipeline` tool lets you define a multi-step process before running it. You kick off the sequence with `run_pipeline`, giving you full control over execution. This is key for building workflows that need guaranteed steps, like payment processing or data validation. The MCP Server handles the state transitions automatically.

Build reliable chatbots using the MCP Server

Start a new conversational agent by calling `create_chatbot`. You then interact with it directly via `run_chatbot` to get answers. If you need more context, remember that you can also upload files to boost its knowledge base using `upload_chatbot_files`.

Execute transformations for Mastra AI

Use the `create_transformation` tool to define custom logic in Python or JavaScript. When the time comes, call `run_transformation` with specific inputs. This lets you pre-process data—say, cleaning a spreadsheet before it gets indexed—before any major workflow step runs.

Setup guide

Set up VectorShift (AI Workflow & RAG Automation) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All VectorShift (AI Workflow & RAG Automation) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "vectorshift-ai-workflow-rag-automation-mcp-client",
  servers: {
    "vectorshift-ai-workflow-rag-automation-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "VectorShift (AI Workflow & RAG Automation) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to VectorShift (AI Workflow & RAG Automation) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent VectorShift (AI Workflow & RAG Automation) transactions"
);
console.log(result.text);

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Common questions about VectorShift (AI Workflow & RAG Automation) MCP in Mastra AI

The MCP Server supports advanced state management. You can use `pause_pipeline` and then `resume_pipeline` to manually intervene, or rely on the workflow's ability to track failures for retries.

The MCP Server indexes knowledge base documents. These are files and URLs, which give your workflows the necessary external context to execute complex decisions.

Absolutely. You can check the status of any workflow or chatbot using `get_pipeline` or `get_chatbot`. This gives you visibility into what's happening behind the scenes.

You have `terminate_pipeline` for stopping an active workflow, or you can use `delete_chatbot` if the conversational agent is no longer needed.

You'll use specific deletion methods. For instance, `delete_knowledge_base_documents` lets you scrub specific files by ID without deleting the whole knowledge base.

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