MCP Server for Milvus

@zilliztech

Repository

Basic information

About MCP Server for Milvus

Model Context Protocol Servers for Milvus

#mcp #milvus #llm-integration

MCP Server for Milvus config

Add this server to your MCP-compatible client using the configuration below.

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

MCP Server for Milvus tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP Server for Milvus?

The MCP Server for Milvus is a Model Context Protocol (MCP) server that provides access to Milvus vector database functionality. It enables LLM applications like Claude Desktop, Cursor, and custom MCP clients to interact with Milvus for vector search, text search, and collection management.

How to use MCP Server for Milvus?

Run the server with uv and a running Milvus instance: uv run src/mcp_server_milvus/server.py --milvus-uri http://localhost:19530. Alternatively, set environment variables in a .env file in src/mcp_server_milvus/. The server supports three communication modes: stdio (default), SSE, and Streamable HTTP, selectable via command-line flags.

Key features of MCP Server for Milvus

Provides full-text, vector, hybrid, and similarity search tools
Allows querying collections with filter expressions
Supports creating collections with quick or custom schema
Lists all collections in the database
Works with Claude Desktop and Cursor via MCP
Offers stdio, SSE, and Streamable HTTP transport modes

Use cases of MCP Server for Milvus

AI-powered code editors performing semantic code search in a vector database
Chat interfaces retrieving relevant documents via hybrid search
Custom AI workflows that query and manage vector collections
Automated collection creation and schema setup for new projects

Frequently asked questions

What are the transport modes and how do they differ?

Stdio mode communicates over standard input/output (default). SSE mode uses HTTP Server-Sent Events for multiple clients. Streamable HTTP mode provides HTTP with streaming support, recommended for production, and optionally supports stateless operation.

What prerequisites are required to run this server?

Python 3.10 or higher, a running Milvus instance (local or remote), and `uv` (recommended) installed. No additional server dependencies are required beyond those.

What Milvus version is required?

The server works with any standard Milvus instance. Note that the `milvus_text_similarity_search` tool requires Milvus 2.6.0 or above and an embedding function configured on the server.

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