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MCP Server Milvus
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MCP Server Milvus

This project is a server based on the Model Context Protocol (MCP), which realizes seamless integration with the Milvus vector database and provides a standardized data access interface for LLM applications. It supports direct invocation of Milvus's search, query, and management functions in applications such as Claude Desktop and Cursor.
DatabaseDeveloper tools#Vector Database#LLM Integration#Search Tool#Data Management.Python
2.5 points
9.1K
Open Site

Overview

Installation

Content Details

Alternatives

What is MCP Server for Milvus?

MCP Server for Milvus is a bridge service that connects large language model (LLM) applications with the Milvus vector database. It follows the standard Model Context Protocol, allowing AI applications to easily access and manipulate data in the vector database.

How to use MCP Server for Milvus?

You can connect this service to MCP-supported AI applications (such as Claude Desktop or Cursor editor) through simple configuration. After the service is running, the AI assistant can directly query and operate your Milvus database.

Use Cases

Suitable for scenarios where AI assistants need to access vectorized data, such as document retrieval, knowledge base Q&A, code search, personalized recommendation and other AI enhancement functions.

Main Features

Vector Similarity Search
Supports vector search based on multiple metrics such as cosine similarity and L2 distance
Hybrid Search
Combines the compound search ability of vector similarity and attribute filtering
Collection Management
Complete collection lifecycle management such as creating, loading, and querying collection information
Data Operations
Supports inserting, updating, deleting data and batch operations
Advantages
Standardized interface: Follows the MCP protocol and is compatible with multiple AI applications
Comprehensive functions: Covers all core functions of Milvus
Easy to integrate: Can be connected to the existing AI workflow with simple configuration
Performance optimization: Specifically optimized for LLM application scenarios
Limitations
Requires pre - installation of the Milvus database
Some advanced functions require Milvus Pro support
Large - scale data operations may require additional performance tuning

How to Use

Installation Preparation
Ensure that Python 3.10+ and the Milvus database are installed
Get the Service Code
Clone the GitHub repository or download the release package
Start the Service
Run the service using the uv tool and specify the Milvus connection address
Configure the AI Application
Add the MCP server configuration in Claude Desktop or Cursor

Usage Examples

Document Retrieval
Let the AI assistant find relevant information from your document library
Code Search
Search for implementations of similar functions in the codebase
Knowledge Base Enhancement
Enhance the AI's answering ability with your proprietary knowledge

Frequently Asked Questions

Do I need to install Milvus myself?
Which AI applications are supported?
How to confirm that the service is running normally?

Related Resources

Milvus Official Documentation
Complete documentation for the Milvus vector database
MCP Protocol Specification
Technical specification of the Model Context Protocol
GitHub Repository
Source code and issue tracking for this project

Installation

Copy the following command to your Client for 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"
      ]
    }
  }
}

{
     "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"
         ]
       }
     }
   }
Note: Your key is sensitive information, do not share it with anyone.

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