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

Q: Can I test multiple workflows at once with VectorShift (AI Workflow & RAG Automation) in Cursor?

Yes. The bulkrunpipeline tool lets you execute many pipeline instances in parallel. You can quickly verify how your code handles varying inputs across a large batch of data.

Q: If I need to update the source data used by VectorShift (AI Workflow & RAG Automation) for my project, what should I do?

You use indexknowledgebase to add new files or URLs. Once the data is indexed into the knowledge base, your code can query it immediately using queryknowledgebase.

Q: Does the MCP Server support different types of logic in Cursor?

It does. Beyond standard queries, you have access to createtransformation and runtransformation. This lets your code execute custom Python or JS scripts on the data before it gets used.

Inject live API data into your code using VectorShift in Cursor.

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

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Run real-time RAG queries and build context within Cursor.

Building an app that needs up-to-date company info? Use `query_knowledge_base` to fetch semantic search results. The unique power here is that the live result gets injected directly into your code, so you're not dealing with stubs or mock data—you're using real context. You can also list available knowledge bases using `list_knowledge_bases`, making sure your function targets the right source.

Orchestrate multi-step workflows directly in Cursor.

Don't write boilerplate for complex logic. Use `create_pipeline` to build the workflow, and then call it using `run_pipeline`. This lets your agent mode execute a full process—like gathering data, transforming it with `run_transformation`, and finally presenting the result in your script. If you need to adjust the pipeline inputs while coding, fetching details via `get_pipeline` is straightforward.

Control chatbot creation and management from Cursor.

Need a quick proof-of-concept chat endpoint? Call `create_chatbot`. Once it's built, you can test its functionality immediately using `run_chatbot`, which sends messages and returns the full response. This gives you live data to integrate into your API calls. When you're done testing, remember you can delete it with `delete_chatbot` to keep your project clean.

Setup guide

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

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the VectorShift (AI Workflow & RAG Automation) MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent VectorShift (AI Workflow & RAG Automation) transactions." If the MCP tools are loaded correctly, Cursor will call the VectorShift (AI Workflow & RAG Automation) tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "vectorshift-ai-workflow-rag-automation-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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 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 Cursor

It injects live data and real API responses directly into the code you're writing. Instead of relying on mock data, your code interacts with actual knowledge bases or running pipelines via the agent mode.

Yes. The `bulk_run_pipeline` tool lets you execute many pipeline instances in parallel. You can quickly verify how your code handles varying inputs across a large batch of data.

You use `index_knowledge_base` to add new files or URLs. Once the data is indexed into the knowledge base, your code can query it immediately using `query_knowledge_base`.

It does. Beyond standard queries, you have access to `create_transformation` and `run_transformation`. This lets your code execute custom Python or JS scripts on the data before it gets used.

The server touches knowledge base documents. If you need to remove sensitive info, use `delete_knowledge_base_documents` to target specific document IDs within your project's context.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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