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

Q: How does Marqo AI (Vector Search & Embeddings) perform semantic search in Claude Desktop?

It uses tensorsearch to convert your text into vector embeddings. Claude then finds documents with similar conceptual meanings, returning results that match your actual intent.

Q: Can I check my index size using Marqo AI (Vector Search & Embeddings) in Claude Desktop?

Yes, just call getindexstats. It returns the document count and index configuration directly in your chat window.

Q: How do I list my existing indices with Marqo AI (Vector Search & Embeddings) and Claude Desktop?

Simply trigger the listindexes tool. Claude will display all available collections so you know exactly which index to query.

Search your local data with Claude Desktop by connecting the Marqo AI (Vector Search & Embeddings) MCP server.

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Connect Marqo AI (Vector Search & Embeddings) MCP to Claude Desktop

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Setup Marqo AI (Vector Search & Embeddings) with Claude Desktop

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Search unstructured data instantly

Run `tensor_search` directly from your Claude chat to find relevant information across your vector indices without leaving the application. It maps your natural language questions to the most accurate results stored in your collection. Stop guessing how to query your data. Your agent interprets the intent behind your prompt and executes the search against your specific index, providing exact matches based on meaning rather than just keywords.

Manage your indices inside Claude

Use `create_index` and `list_indexes` to organize your vector space without jumping into a separate terminal. You keep your entire workflow contained within the desktop app. Check your index health with `get_index_stats` to ensure your data is ready for queries. It provides the metadata you need to verify that your documents are correctly indexed before you run your next search.

Update your search catalog

Sync your data by using `add_documents` to push new content into your Marqo indices. Claude handles the ingestion process, keeping your semantic engine up to date with your latest project notes or technical documentation. Remove outdated or incorrect records using `delete_documents` to keep your search results clean. You control exactly what data stays in your vector database, ensuring that your AI client always references the most current information.

Setup guide

Set up Marqo AI (Vector Search & Embeddings) MCP in Claude Web or Desktop

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.
3

Start a conversation

Open a new chat. The Marqo AI (Vector Search & Embeddings) MCP tools are available immediately — no restart needed.

Endpoint URL

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

No configuration file needed — paste the URL directly in the Claude web interface.

Available on Free (1 connector), Pro, Max, Team, and Enterprise plans.

Prerequisites

Claude Desktop installed (macOS or Windows)
Active Vinkius subscription with a valid endpoint token

1

Open Claude Desktop Settings

Click the menu icon at the top-left corner, go to Settings → Developer → Edit Config. This opens claude_desktop_config.json in your default text editor.
2

Paste the Marqo AI (Vector Search & Embeddings) MCP configuration

Copy the JSON snippet on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Desktop

Close and reopen the application. Claude needs a full restart to load new MCPs — refreshing a conversation is not enough.
4

Verify the connection

Open a new conversation. Click the 🔌 icon at the bottom of the message input. You should see tools listed under marqo-ai-vector-search-embeddings-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 Claude Desktop

It uses `tensor_search` to convert your text into vector embeddings. Claude then finds documents with similar conceptual meanings, returning results that match your actual intent.

Yes, just call `get_index_stats`. It returns the document count and index configuration directly in your chat window.

Simply trigger the `list_indexes` tool. Claude will display all available collections so you know exactly which index to query.

Your data remains within your specified Marqo instance. The server only transmits the specific queries and documents you explicitly send through the MCP interface.

No, this MCP server requires a network connection to communicate with your hosted Marqo instance. It cannot function without an active API link.

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