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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Milvus 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 Milvus. perform ANN searches, query scalar entities, 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 · 7 capabilities

The complete Milvus capability set.

These are the exact actions your AI can choose when you ask it to work with Milvus.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Milvus.

  1. 01

    List collections

    Always query this first. List index collections tracked inside the Milvus Vector Database

  2. 02

    Describe collection

    Explore the explicit schema mapping and indexing definition of a Milvus collection

  3. 03

    Get entities

    Extract unique vector items bounding exactly by known Primary Keys

  4. 04

    Delete entities

    Irreversibly delete specific vector records utilizing primary keys

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Milvus.

  1. 05

    Get collection stats

    Get collection statistics bounding row counts natively

  2. 06

    Query entities

    Query explicitly using scalar expressions to retrieve entities

  3. 07

    Search vectors

    Make sure to feed a strict explicit JSON Array matching exact dimensions. Search nearest vector neighbors matching implicit embedding inputs

Observed, not estimated

891ms average. Fast in production.

Milvus is checked daily against the live service.

Daily averagePeak 1039ms
Aug 20Today
Fastest day
702ms
Slowest day
1039ms
14-day trend
Slowing+18%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 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 Milvus, so you can see the experience inside your AI.

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

Milvus Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/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 — Milvus capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "milvus-open-source-vector-database-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/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 Milvus owners ask.

  • 01

    How do I perform an ANN search through my agent?

    Use the search_vectors capability by providing the collection name and a JSON float array matching the collection's dimensions. Your agent will perform an Approximate Nearest Neighbor search and return the most semantically relevant entities.

  • 02

    Can I filter results using structured fields instead of just vectors?

    Yes. Use the query_entities capability with a Milvus-style filter expression. This allows you to retrieve entities based on primary keys, tags, or other scalar fields without necessarily performing a vector similarity search.

  • 03

    How do I check the schema and dimension requirements for a Milvus collection?

    The describe_collection capability retrieves the complete schema mapping. Your agent will report the required vector dimensions, index types, and primary key names, helping you ensure your search queries are compatible with the database logic.

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