How to Use the Marqo AI (Vector Search & Embeddings) MCP in Cursor

Q: How does Marqo AI (Vector Search & Embeddings) help Cursor agents find relevant code context?

The agent uses tensorsearch to query your documents. It retrieves information based on the semantic meaning of your prompt, which it then uses to generate relevant code blocks.

Q: Can I manage my indices with Marqo AI (Vector Search & Embeddings) inside Cursor?

Yes, you can use listindexes and createindex to handle your collection management. It keeps your database operations within the same environment as your code.

Q: Does Marqo AI (Vector Search & Embeddings) in Cursor support bulk data updates?

Yes, you can call adddocuments to push data into your index. It is the primary way to keep your vector search results synced with your latest project data.

Use Marqo AI (Vector Search & Embeddings) in Cursor to inject live semantic context into your codebase.

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Connect Marqo AI (Vector Search & Embeddings) MCP to Cursor

Create your Vinkius account to connect Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) with Cursor

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Inject semantic search into code

Let your Cursor agent call `tensor_search` to pull real product or documentation data into your active file. It avoids the need for hardcoded mocks by fetching live semantic matches while you code. Your agent uses these search results to write code that actually reflects your current data structure. It makes your development process faster by providing context that stays grounded in your existing vector indices.

Automate index maintenance in Cursor

Call `create_index` or `delete_documents` directly from your chat sidebar to manage your database state. You don't have to leave your editor to clean up or build out new search collections. Keep your search infrastructure aligned with your code changes. By using these tools inside Cursor, you ensure that your data layer evolves alongside your project logic.

Verify data ingestion during builds

Run `add_documents` to populate your Marqo index as part of your development workflow. You can immediately verify the ingestion by calling `get_index_stats` to see the updated document count. This workflow allows you to test your search logic in real-time. You see how your changes affect the search quality without context switching between your code and a database management console.

Setup guide

Set up Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) transactions." If the MCP tools are loaded correctly, Cursor will call the Marqo AI (Vector Search & Embeddings) tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "marqo-ai-vector-search-embeddings-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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 Marqo AI. 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 Marqo AI (Vector Search & Embeddings) MCP in Cursor

The agent uses `tensor_search` to query your documents. It retrieves information based on the semantic meaning of your prompt, which it then uses to generate relevant code blocks.

Yes, you can use `list_indexes` and `create_index` to handle your collection management. It keeps your database operations within the same environment as your code.

Yes, you can call `add_documents` to push data into your index. It is the primary way to keep your vector search results synced with your latest project data.

Vinkius handles your authentication tokens. Your local Cursor config only references the endpoint, keeping your credentials out of your repository.

The tool returns an error message in your chat window. You can then adjust your `tensor_search` parameters or check your connection status.

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