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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use MongoDB Atlas Vector Search with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Manage vector storage via MongoDB Atlas. perform similarity searches, query MQL documents, and audit collections.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 6 capabilities

The complete MongoDB Atlas Vector Search capability set.

These are the exact actions your AI can choose when you ask it to work with MongoDB Atlas Vector Search.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through MongoDB Atlas Vector Search.

  1. 01

    Search

    Perform highly-dimensional Vector similarity search using $vectorSearch

  2. 02

    Create index

    Create literal standard embedding Search Index bound to dimensions

  3. 03

    Delete

    Delete literal documents bounded by the parsed MongoDB filters

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through MongoDB Atlas Vector Search.

  1. 04

    Find

    Find standard MongoDB documents resolving standard query filters

  2. 05

    Insert

    Insert a distinct generic document into standard target collection

  3. 06

    List collections

    List accessible data collections bound explicitly inside Atlas limits

Observed, not estimated

875ms average. Fast in production.

MongoDB Atlas Vector Search is checked daily against the live service.

Daily averagePeak 1020ms
Aug 20Today
Fastest day
676ms
Slowest day
1020ms
14-day trend
Slowing+29%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of MongoDB Atlas Vector Search, so you can see the experience inside your AI.

It does not authenticate your account with MongoDB Atlas Vector Search. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

MongoDB Atlas Vector Search Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_rMRfujNKzotBfoPOFvzesaIZTdVkigbmunZrRwce/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — MongoDB Atlas Vector Search capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "mongodb-atlas-vector-search-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_rMRfujNKzotBfoPOFvzesaIZTdVkigbmunZrRwce/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions MongoDB Atlas Vector Search owners ask.

  • 01

    Can I manage both vector search and standard data in the same conversation?

    Yes. MongoDB Atlas Vector Search is unified. You can use the search capability for similarity and the find or insert capabilities for standard operational data management using MQL, allowing you to bridge both worlds natively.

  • 02

    How do I create a new vector search index through the agent?

    Use the create_index capability by providing the database, collection, and required dimensions (matching your embedding model). Your agent will provision the index infrastructure on Atlas to enable high-speed vector retrieval.

  • 03

    Can my agent find specific documents using standard MongoDB query filters?

    Absolutely. Use the find capability with a JSON string representing your MQL filter (e.g. {"status":"active"}). Your agent will execute the Data API request and return the matching documents and their scalar properties securely.

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