Milvus (Open-Source Vector Database) Connector for AI agents.

Q: How do I perform an ANN search through my agent?

Use the searchvectors tool 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.

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

Yes. Use the queryentities tool 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.

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

The describecollection tool 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.

7 live capabilities

Query your high-performance vector embeddings and manage production search indexes.

Live agent request Milvus (Open-Source Vector Database) / Connector

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AI Agent

Why people use Milvus (Open-Source Vector Database)

Milvus Vector Database Management for ML Engineers

This Connector lets you stay in your workspace. You can ask your agent to list collections, check stats, or describe schemas in plain English. You get instant answers without the overhead of manual command-line work.

What Vinkius changes

You get a natural language interface for your vector database management and search.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

Real-world use case 01

Debugging Embedding Dimensions

An ML engineer isn't sure if their new model's embeddings match the database.
Real-world use case 02

Production Monitoring

A search architect needs to see if a recent data load spiked memory usage.
Real-world use case 03

Hybrid Search Testing

A developer wants to see results for 'blue shoes' but only for the 'summer' tag.

Complete set · 7capabilities

The complete Milvus (Open-Source Vector Database) capability set.

These are the exact actions your AI can choose when you ask it to work with Milvus (Open-Source Vector Database).

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Milvus (Open-Source Vector Database).

01 Capability
List collections
See every vector collection currently tracked in your vector database. This helps you get a clear overview of your data structure at a glance.
02 Capability
Describe collection
View the explicit schema mapping and indexing definitions for a specific collection. This is useful for verifying that your dimensions and keys are set up correctly.
03 Capability
Search vectors
Perform nearest neighbor searches by providing an array of embedding vectors. This lets you find the most relevant matches for your user queries instantly.
04 Capability
Query entities
Retrieve specific entities using scalar expressions like tags or dates. This allows you to filter your search results by structured metadata.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Milvus (Open-Source Vector Database).

05 Capability
Get entities
Extract unique vector items that match a specific primary key. You can use this to audit exact data points without relying on semantic similarity.
06 Capability
Get collection stats
Get real-time metrics for row counts and physical memory usage. You can use this to monitor the health and scale of your production indices.
07 Capability
Delete entities
Irreversibly delete specific vector records using their primary identifiers. This makes it easy to keep your search index clean and optimized.

Set up in minutes

One URL. Then ask Milvus (Open-Source Vector Database) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Milvus (Open-Source Vector Database) from the conversation.

Choose your client

Live preview

Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp

Step 01

Open Connectors

In Claude Web or Claude Desktop, open Settings and choose Connectors.
Step 02

Add the URL

Choose Add custom connector, name it Milvus (Open-Source Vector Database), and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable Milvus (Open-Source Vector Database) for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp

Step 01

Open MCP settings

On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
Step 02

Add the URL

Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Milvus (Open-Source Vector Database) URL.
Step 03

Save and start

Save the connection and enable Milvus (Open-Source Vector Database) in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "milvus-open-source-vector-database": {
      "url": "https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp"
    }
  }
}

Step 01

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Step 02

Add the server config

Paste the JSON configuration above into the mcp.json file that opens
Step 03

Save the file

Cursor will automatically detect the new Connector
Step 04

Start using Milvus (Open-Source Vector Database)

Open Agent mode in chat and ask: "Using Milvus (Open-Source Vector Database), help me...". 7 tools available

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "milvus-open-source-vector-database": {
      "url": "https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp"
    }
  }
}

Step 01

Create MCP config

Create a .vscode/mcp.json file in your project root
Step 02

Add the server config

Paste the JSON configuration above
Step 03

Enable Agent mode

Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Step 04

Start using Milvus (Open-Source Vector Database)

Ask Copilot: "Using Milvus (Open-Source Vector Database), help me...". 7 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "milvus-open-source-vector-database": {
      "url": "https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp"
    }
  }
}

Step 01

Open MCP Settings

Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Step 02

Add the server

Paste the JSON configuration above into mcp_config.json
Step 03

Save and reload

Windsurf will detect the new server automatically
Step 04

Start using Milvus (Open-Source Vector Database)

Open Cascade and ask: "Using Milvus (Open-Source Vector Database), help me...". 7 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "milvus-open-source-vector-database": {
      "url": "https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp"
    }
  }
}

Step 01

Open Cline MCP Settings

Click the Connectors icon in the Cline sidebar panel
Step 02

Add remote server

Click "Add Connector" and paste the configuration above
Step 03

Enable the server

Toggle the server switch to ON
Step 04

Start using Milvus (Open-Source Vector Database)

Ask Cline: "Using Milvus (Open-Source Vector Database), help me...". 7 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add milvus-open-source-vector-database --transport http "https://edge.vinkius.com/vk_preview_xJCavC8CNaxFuBWU15XnlYyQ3g4Cn853dcyYbM82/mcp"

Step 01

Install Claude Code

Run npm install -g @anthropic-ai/claude-code if not already installed
Step 02

Add the Connector

Run the command above in your terminal
Step 03

Verify the connection

Run claude mcp to list connected servers, or type /mcp inside a session
Step 04

Start using Milvus (Open-Source Vector Database)

Ask Claude: "Using Milvus (Open-Source Vector Database), show me...". 7 tools are ready

Where the request belongs

Work Milvus can move forward.

Built around the request

This is for the ML engineer who is tired of writing Python scripts to verify embedding dimensions or the search architect who needs to monitor index health without leaving their IDE.

ML Engineer

You use this to verify that your new model's embeddings match the database dimensions and to test similarity scores.

Search Architect

You use this to audit complex schemas and monitor memory usage across multiple production environments.

Software Developer

You use this to quickly prototype hybrid searches and manage vector lifecycles during the development phase.

Build the capability set

Each Connector adds new actions and data without changing how you work.

Browse Connectors

01 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector

02 10 capabilities

Zilliz Cloud

Manage vector collections and perform similarity searches via Zilliz Cloud.

View Connector

03 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector

04 6 capabilities

Elasticsearch Vector

Empower vector search via Elasticsearch. perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

View Connector

05 6 capabilities

Redis Vector

Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.

View Connector

06 7 capabilities

Weaviate

Search and manage vector data on Weaviate. the AI-native database for building production-grade AI applications.

View Connector

Bring your own AI

Change the model, client or framework. Keep Milvus connected.

Claude
ChatGPT
Gemini
Cursor
VS Code
Windsurf
ZCode
Cline
Zed
Continue
Kiro
Roo Code
Zencoder
Goose
Void
Augment Code
Amp
Qodo
Tabnine
Pieces
Sourcegraph Cody
JetBrains
Warp
Amazon Q
Antigravity
BoltAI
Raycast
Jan
LM Studio
AnythingLLM
Open WebUI
Msty
Cherry Studio
LibreChat
TypingMind
Chorus
5ire
n8n
LangChain
LlamaIndex
CrewAI
Vercel AI SDK

Before you connect

Questions about Milvus.

The practical details behind the request, access and result.

Can I use the Milvus MCP to check my database health?

Yes, you can use it to see real-time stats like row counts and memory usage. This helps you monitor the health and scale of your production indices without manual checks.

How does Milvus MCP handle metadata filters?

It allows your agent to combine vector searches with specific scalar filters like tags, IDs, or dates. This makes it easy to perform hybrid searches using natural language.

Can I delete specific records with this?

Yes, you can remove vector items by their primary keys to keep your index clean and optimized. This is much faster than deleting items through a web UI.

Is this good for checking my schema?

Definitely. You can get the full schema mapping and index definitions for any collection. It's a great way to verify that your dimensions and keys are set up correctly.

Can I use this with Zilliz Cloud?

Yes, the Connector supports both standard Milvus and Zilliz Cloud tokens, making it compatible with your existing cloud infrastructure.

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.

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.

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.

One connection away

Give your agent a direct line to Milvus.

Connect Milvus once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available

Milvus Vector Database Management for ML Engineers

You get a natural language interface for your vector database management and search.

Debugging Embedding Dimensions

Production Monitoring

Hybrid Search Testing

List collections

Describe collection

Search vectors

Query entities

Get entities

Get collection stats

Delete entities

Open Connectors

Add the URL

Turn it on in chat

Open MCP settings

Add the URL

Save and start

Open MCP Settings

Add the server config

Save the file

Start using Milvus (Open-Source Vector Database)

Create MCP config

Add the server config

Enable Agent mode

Start using Milvus (Open-Source Vector Database)

Open MCP Settings

Add the server

Save and reload

Start using Milvus (Open-Source Vector Database)

Open Cline MCP Settings

Add remote server

Enable the server

Start using Milvus (Open-Source Vector Database)

Install Claude Code

Add the Connector

Verify the connection

Start using Milvus (Open-Source Vector Database)

ML Engineer

Search Architect

Software Developer

Qdrant

Zilliz Cloud

OpenSearch Vector

Elasticsearch Vector

Redis Vector

Weaviate

Give your agent a direct line to Milvus.