MCP + Milvus: Connecting AI with Vector Databases

Introduction

The Model Context Protocol (MCP) is an open protocol that enables AI applications, such as Claude and Cursor, to interact with external data sources and tools seamlessly. Whether you’re building custom AI applications, integrating AI workflows, or enhancing chat interfaces, MCP provides a standardized way to connect large language models (LLMs) with relevant contextual data.

This tutorial walks you through setting up an MCP server for Milvus, allowing AI applications to perform vector searches, manage collections, and retrieve data using natural language commands—without writing custom database queries.

Prerequisites

Before setting up the MCP server, ensure you have:

• Python 3.10 or higher
• A running Milvus instance
• uv (recommended for running the server)

Getting Started

The recommended way to use this MCP server is to run it directly with uv without installation. This is how both Claude Desktop and Cursor are configured to use it in the examples below.

If you want to clone the repository:

git clone https://github.com/zilliztech/mcp-server-milvus.git
cd mcp-server-milvus

Then you can run the server directly:

uv run src/mcp_server_milvus/server.py --milvus-uri http://localhost:19530

Supported Applications

This MCP server can be used with various AI applications that support the Model Context Protocol, such as:

• Claude Desktop: Anthropic’s desktop application for Claude
• Cursor: AI-powered code editor with MCP support in its Composer feature
• Other custom MCP clients Any application implementing the MCP client specification

Using MCP with Claude Desktop

1. Install Claude Desktop.
2. Open the Claude configuration file:
   • On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
3. Add the following configuration:

{
  "mcpServers": {
    "milvus": {
      "command": "/PATH/TO/uv",
      "args": [
        "--directory",
        "/path/to/mcp-server-milvus/src/mcp_server_milvus",
        "run",
        "server.py",
        "--milvus-uri",
        "http://localhost:19530"
      ]
    }
  }
}

4. Restart Claude Desktop to apply the changes.

Using MCP with Cursor

Cursor also supports MCP tools through its Agent feature in Composer. You can add the Milvus MCP server to Cursor in two ways:

Option 1: Using Cursor Settings UI

1. Open Cursor Settings → Features → MCP.
2. Click + Add New MCP Server.
3. Fill in:
   • Type: stdio
   • Name: milvus
   • Command:

     /PATH/TO/uv --directory /path/to/mcp-server-milvus/src/mcp_server_milvus run server.py --milvus-uri http://127.0.0.1:19530
     

   • ⚠️ Tip: Use 127.0.0.1 instead of localhost to avoid potential DNS resolution issues.

Option 2: Using Project-specific Configuration (Recommended)

1. Create a .cursor/mcp.json file in your project root directory:

{
  "mcpServers": {
    "milvus": {
      "command": "/PATH/TO/uv",
      "args": [
        "--directory",
        "/path/to/mcp-server-milvus/src/mcp_server_milvus",
        "run",
        "server.py",
        "--milvus-uri",
        "http://127.0.0.1:19530"
      ]
    }
  }
}

2. Restart Cursor to apply the configuration.

After adding the server, you may need to press the refresh button in the MCP settings to populate the tool list. The Composer Agent will automatically use the Milvus tools when relevant to your queries.

Verifying the Integration

To ensure the MCP server is correctly set up:

For Cursor

1. Go to Cursor Settings → Features → MCP.
2. Confirm that "Milvus" appears in the list of MCP servers.
3. Check if Milvus tools (e.g., milvus_list_collections, milvus_vector_search) are listed.
4. If errors appear, see the Troubleshooting section below.

MCP Server Tools for Milvus

This MCP server provides multiple tools for searching, querying, and managing vector data in Milvus. For more details, please refer to the mcp-server-milvus documentation.

🔍 Search and Query Tools

• milvus-text-search → Search for documents using full text search.
• milvus-vector-search → Perform vector similarity search on a collection.
• milvus-hybrid-search → Perform hybrid search combining vector similarity and attribute filtering.
• milvus-multi-vector-search → Perform vector similarity search with multiple query vectors.
• milvus-query → Query collection using filter expressions.
• milvus-count → Count entities in a collection.

📁 Collection Management

• milvus-list-collections → List all collections in the database.
• milvus-collection-info → Get detailed information about a collection.
• milvus-get-collection-stats → Get statistics about a collection.
• milvus-create-collection → Create a new collection with specified schema.
• milvus-load-collection → Load a collection into memory for search and query.
• milvus-release-collection → Release a collection from memory.
• milvus-get-query-segment-info → Get information about query segments.
• milvus-get-collection-loading-progress → Get the loading progress of a collection.

📊 Data Operations

• milvus-insert-data → Insert data into a collection.
• milvus-bulk-insert → Insert data in batches for better performance.
• milvus-upsert-data → Upsert data into a collection (insert or update if exists).
• milvus-delete-entities → Delete entities from a collection based on filter expression.
• milvus-create-dynamic-field → Add a dynamic field to an existing collection.

⚙️ Index Management

• milvus-create-index → Create an index on a vector field.
• milvus-get-index-info → Get information about indexes in a collection.

Environment Variables

• MILVUS_URI → Milvus server URI (can be set instead of --milvus-uri).
• MILVUS_TOKEN → Optional authentication token.
• MILVUS_DB → Database name (defaults to “default”).

Development

To run the server directly:

uv run server.py --milvus-uri http://localhost:19530

Examples

Using Claude Desktop

Example 1: Listing Collections

What are the collections I have in my Milvus DB?

Claude will then use MCP to check this information on our Milvus DB.

I'll check what collections are available in your Milvus database.

> View result from milvus-list-collections from milvus (local)

Here are the collections in your Milvus database:

1. rag_demo
2. test
3. chat_messages
4. text_collection
5. image_collection
6. customized_setup
7. streaming_rag_demo

Example 2: Searching for Documents

Find documents in my text_collection that mention "machine learning"

Claude will use the full-text search capabilities of Milvus to find relevant documents:

I'll search for documents about machine learning in your text_collection.

> View result from milvus-text-search from milvus (local)

Here are the documents I found that mention machine learning:
[Results will appear here based on your actual data]

Using Cursor

Example: Creating a Collection

In Cursor’s Composer, you can ask:

Create a new collection called 'articles' in Milvus with fields for title (string), content (string), and a vector field (128 dimensions)

Cursor will use the MCP server to execute this operation:

I'll create a new collection called 'articles' with the specified fields.

> View result from milvus-create-collection from milvus (local)

Collection 'articles' has been created successfully with the following schema:
- title: string
- content: string
- vector: float vector[128]

Troubleshooting

Common Issues

Connection Errors

If you see errors like "Failed to connect to Milvus server":

1. Verify your Milvus instance is running: docker ps (if using Docker)
2. Check the URI is correct in your configuration
3. Ensure there are no firewall rules blocking the connection
4. Try using 127.0.0.1 instead of localhost in the URI

Authentication Issues

If you see authentication errors:

1. Verify your MILVUS_TOKEN is correct
2. Check if your Milvus instance requires authentication
3. Ensure you have the correct permissions for the operations you’re trying to perform

Tool Not Found

If the MCP tools don’t appear in Claude Desktop or Cursor:

1. Restart the application
2. Check the server logs for any errors
3. Verify the MCP server is running correctly
4. Press the refresh button in the MCP settings (for Cursor)

Getting Help

If you continue to experience issues:

1. Check the GitHub Issues for similar problems
2. Join the Zilliz Community Discord for support
3. File a new issue with detailed information about your problem

Conclusion

By following this tutorial, you now have an MCP server running, enabling AI-powered vector search in Milvus. Whether you’re using Claude Desktop or Cursor, you can now query, manage, and search your Milvus database using natural language commands—without writing database code!