MCP Server for Milvus

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

This repository contains a MCP server that provides access to Milvus vector database functionality.

Prerequisites

Before using this MCP server, ensure you have:

Usage

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

Alternatively you can change the .env file in the src/mcp_server_milvus/ directory to set the environment variables and run the server with the following command:

uv run src/mcp_server_milvus/server.py

Important: the .env file will have higher priority than the command line arguments.

Running Modes

The server supports two running modes: stdio (default) and SSE (Server-Sent Events).

Stdio Mode (Default)

Description: Communicates with the client via standard input/output. This is the default mode if no mode is specified.

Usage:

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

SSE Mode

Description: Uses HTTP Server-Sent Events for communication. This mode allows multiple clients to connect via HTTP and is suitable for web-based applications.

Usage:

uv run src/mcp_server_milvus/server.py --sse --milvus-uri http://localhost:19530 --port 8000

--sse: Enables SSE mode.
--port: Specifies the port for the SSE server (default: 8000).

Debugging in SSE Mode:

If you want to debug in SSE mode, after starting the SSE service, enter the following command:

mcp dev src/mcp_server_milvus/server.py

The output will be similar to:

% mcp dev src/mcp_server_milvus/merged_server.py
Starting MCP inspector...
⚙️ Proxy server listening on port 6277
🔍 MCP Inspector is up and running at http://127.0.0.1:6274 🚀

You can then access the MCP Inspector at http://127.0.0.1:6274 for testing.

Supported Applications

This MCP server can be used with various LLM applications that support the Model Context Protocol:

Claude Desktop: Anthropic's desktop application for Claude
Cursor: AI-powered code editor with MCP support
Custom MCP clients: Any application implementing the MCP client specification

Usage with Claude Desktop

Configuration for Different Modes

SSE Mode Configuration

Follow these steps to configure Claude Desktop for SSE mode:

Install Claude Desktop from https://claude.ai/download.
Open your Claude Desktop configuration file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Add the following configuration for SSE mode:

{
  "mcpServers": {
    "milvus-sse": {
      "url": "http://your_sse_host:port/sse",
      "disabled": false,
      "autoApprove": []
    }
  }
}

Restart Claude Desktop to apply the changes.

Stdio Mode Configuration

For stdio mode, follow these steps:

Install Claude Desktop from https://claude.ai/download.
Open your Claude Desktop configuration file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Add the following configuration for stdio mode:

{
  "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"
      ]
    }
  }
}

Restart Claude Desktop to apply the changes.

Usage with Cursor

Cursor also supports MCP tools. You can integrate your Milvus MCP server with Cursor by following these steps:

Integration Steps

Open Cursor Settings > MCP
Click on Add new global MCP server
After clicking, it will automatically redirect you to the mcp.json file, which will be created if it doesn’t exist

Configuring the `mcp.json` File

For Stdio Mode:

Overwrite the mcp.json file with the following content:

{
  "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"
      ]
    }
  }
}

For SSE Mode:

Start the service by running the following command:
```
uv run src/mcp_server_milvus/server.py --sse --milvus-uri http://your_sse_host --port port
```
Note: Replace http://your_sse_host with your actual SSE host address and port with the specific port number you’re using.

Once the service is up and running, overwrite the mcp.json file with the following content:

{
    "mcpServers": {
      "milvus-sse": {
        "url": "http://your_sse_host:port/sse",
        "disabled": false,
        "autoApprove": []
      }
    }
}

Completing the Integration

After completing the above steps, restart Cursor or reload the window to ensure the configuration takes effect.

Verifying the Integration

To verify that Cursor has successfully integrated with your Milvus MCP server:

Open Cursor Settings > MCP
Check if "milvus" or "milvus-sse" appear in the list（depending on the mode you have chosen）
Confirm that the relevant tools are listed (e.g., milvus_list_collections, milvus_vector_search, etc.)
If the server is enabled but shows an error, check the Troubleshooting section below

Available Tools

The server provides the following tools:

Search and Query Operations

milvus_text_search: Search for documents using full text search
- Parameters:
  - collection_name: Name of collection to search
  - query_text: Text to search for
  - limit: The maximum number of results to return (default: 5)
  - output_fields: Fields to include in results
  - drop_ratio: Proportion of low-frequency terms to ignore (0.0-1.0)
milvus_vector_search: Perform vector similarity search on a collection
- Parameters:
  - collection_name: Name of collection to search
  - vector: Query vector
  - vector_field: Field name for vector search (default: "vector")
  - limit: The maximum number of results to return (default: 5)
  - output_fields: Fields to include in results
  - filter_expr: Filter expression
  - metric_type: Distance metric (COSINE, L2, IP) (default: "COSINE")
milvus_hybrid_search: Perform hybrid search on a collection
- Parameters:
  - collection_name: Name of collection to search
  - query_text: Text query for search
  - text_field: Field name for text search
  - vector: Vector of the text query
  - vector_field: Field name for vector search
  - limit: The maximum number of results to return
  - output_fields: Fields to include in results
  - filter_expr: Filter expression
milvus_text_similarity_search: Perform text similarity search on a collection

Note: This tool is only supported in Milvus 2.6.0 and above. And you need to set the embedding function at the Milvus server. See Embedding Function for more details.
- Parameters:
  - collection_name: Name of collection to search
  - query_text: Text query for similarity search
  - anns_field: Field name for text search
  - limit: The maximum number of results to return (default: 5)
  - output_fields: Fields to include in results
  - metric_type: Distance metric (COSINE, L2, IP) (default: "COSINE")
  - filter_expr: Optional filter expression
milvus_query: Query collection using filter expressions
- Parameters:
  - collection_name: Name of collection to query
  - filter_expr: Filter expression (e.g. 'age > 20')
  - output_fields: Fields to include in results
  - limit: The maximum number of results to return (default: 10)

Collection Management

milvus_list_collections: List all collections in the database
milvus_create_collection: Create a new collection with quick setup or customized schema
- Parameters:
  - collection_name: Name for the new collection
  - auto_id: whether to auto generate id, default to True
  - dimension: vector dimension, default to 768; for quick setup and will be ignored if field_schema is provided
  - primary_field_name: name of the primary field, default to "id"; for quick setup and will be ignored if field_schema is provided
  - vector_field_name: name of the vector field, default to "vector"; for quick setup and will be ignored if field_schema is provided
  - metric_type: metric type, default to "COSINE"; for quick setup and will be ignored if field_schema is provided
  - field_schema: List of field schema, each element is a dictionary with the following keys:
    - name: name of the field
    - type: type of the field
  - index_params: Optional list of index parameters, each element is a dictionary with the following keys:
    - field_name: name of the field to index
    - index_type: index type
    - **kwargs: other optional index parameters
  - other_kwargs: Additional keyword arguments for the collection creation
milvus_load_collection: Load a collection into memory for search and query
- Parameters:
  - collection_name: Name of collection to load
  - replica_number: Number of replicas (default: 1)
milvus_release_collection: Release a collection from memory
- Parameters:
  - collection_name: Name of collection to release
milvus_get_collection_info: Lists detailed information like schema, properties, collection ID, and other metadata of a specific collection.
- Parameters:
  - collection_name: Name of the collection to get detailed information about

Data Operations

milvus_insert_data: Insert data into a collection
- Parameters:
  - collection_name: Name of collection
  - data: Dictionary mapping field names to lists of values
milvus_delete_entities: Delete entities from a collection based on filter expression
- Parameters:
  - collection_name: Name of collection
  - filter_expr: Filter expression to select entities to delete

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 your Milvus DB.

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

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, 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.

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":

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

Authentication Issues

If you see authentication errors:

Verify your MILVUS_TOKEN is correct
Check if your Milvus instance requires authentication
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:

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

Getting Help

If you continue to experience issues:

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

Features

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Features

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