Is RagWithMilvusTest safe to use?

RagWithMilvusTest has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.

How do I install RagWithMilvusTest?

RagWithMilvusTest supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.

What can RagWithMilvusTest do?

RagWithMilvusTest provides 1 tool: search. See the full tools list on the server page for descriptions and parameters.

What AI clients work with RagWithMilvusTest?

RagWithMilvusTest is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.

Is RagWithMilvusTest actively maintained?

RagWithMilvusTest is less actively maintained — last commit was 270 days ago. It has 43 GitHub stars.

RagWithMilvusTest

Name: RagWithMilvusTest
Author: NanGePlus

pymilvus · by NanGePlus

(1)使用低代码平台N8N实现对多个微信公众号文章进行自动采集并保存到本地文件 ; (2)使用主流的开源云原生向量数据库milvus将采集到的数据存储到知识库中并满足语义搜索、全文搜索及混合搜索; (3)将搜索功能封装为标准的MCP Server对外提供服务;(4)大模型(Agent)使用搜索MCP Server进行内容搜索

43 1 tool GitHub PyPI

No known CVEs

MIT license

Stale

Last commit 270d ago

Works with most clients

Transport: stdio

1 tool · ~173 tok

Grade A · 0.1% of 200K ctx

Edit this pageView history

AI / ML Data

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "ragwithmilvustest": {
      "args": [
        "pymilvus"
      ],
      "command": "uvx"
    }
  }
}

Are you the author?

Add this badge to your README to show your security score and help users find safe servers.

Embed in your READMEAbout badges →

[![MCPpedia Score](https://mcppedia.org/api/badge/ragwithmilvustest)](https://mcppedia.org/s/ragwithmilvustest)

Read me

What RagWithMilvusTest does

【从内容采集到智能搜索】全栈开源：手把手构建能理解业务逻辑的企业级AI搜索Agent，低代码采集 + 云原生向量库Milvus + MCP协议 + Agent https://www.bilibili.com/video/BV1whaqzzEK8/

Test This Server

Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.

uvx 'pymilvus' 2>&1 | head -1 && echo "✓ Server started successfully"

After testing, let us know if it worked:

Loading README…

Scored, not listed

Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

78/100across 5 weighted dimensions

How we score →

0255075100

−22

Security

Maintenance

Efficiency

Documentation

Compatibility

Categoriesclick a row to see evidence

Security

OSV.dev

No known CVEs.

Checked pymilvus against OSV.dev.

Inventory

Tools (1)

Click any tool to inspect its schema.

~173 tokens total

Community

Reviews

Be the first to review

Have you used this server?

Share your experience — it helps other developers decide.

How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is RagWithMilvusTest safe to use?: RagWithMilvusTest has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.
How do I install RagWithMilvusTest?: RagWithMilvusTest supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.
What can RagWithMilvusTest do?: RagWithMilvusTest provides 1 tool: search. See the full tools list on the server page for descriptions and parameters.
What AI clients work with RagWithMilvusTest?: RagWithMilvusTest is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.
Is RagWithMilvusTest actively maintained?: RagWithMilvusTest is less actively maintained — last commit was 270 days ago. It has 43 GitHub stars.

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MCP Security Weekly

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Community

Discussion

Start a conversation

Ask a question, share a tip, or report an issue.

Has anyone used this with Cursor?How do you handle auth?Any alternatives?

Edit this pageView history

AI / ML Data

Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "ragwithmilvustest": {
      "args": [
        "pymilvus"
      ],
      "command": "uvx"
    }
  }
}

Are you the author?

Add this badge to your README to show your security score and help users find safe servers.

Embed in your READMEAbout badges →

[![MCPpedia Score](https://mcppedia.org/api/badge/ragwithmilvustest)](https://mcppedia.org/s/ragwithmilvustest)

Read me

What RagWithMilvusTest does

Test This Server

Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.

uvx 'pymilvus' 2>&1 | head -1 && echo "✓ Server started successfully"

After testing, let us know if it worked:

README

1、项目介绍

【从内容采集到智能搜索】全栈开源：手把手构建能理解业务逻辑的企业级AI搜索Agent，低代码采集 + 云原生向量库Milvus + MCP协议 + Agent
https://youtu.be/i3ul7JAAQIY
https://www.bilibili.com/video/BV1whaqzzEK8/

【从内容采集到智能搜索】Milvus Attu图形用户界面可视化管理工具操作指南，创建数据库、定义Schema、创建索引、创建集合、上传本地文件导入数据
https://youtu.be/SaMb4kA7OT0
https://www.bilibili.com/video/BV1sPafzqEEQ/

【从内容采集到智能搜索】N8N低代码平台实现全自动公众号文章内容采集工作流并使用AI Agent自动生成内容摘要
https://youtu.be/bHjI5U3ETLU
https://www.bilibili.com/video/BV17mpBz5ELJ/

AI在企业应用的不尽如人意，不仅仅在于工程上的高要求，也在于开发者往往容易沉浸于某些技术细节，却忽视真实场景的复杂性
以看似简单的AI内容搜索为例，用户的需求远非“搜索下关于AI智能体的文章”这样的单维问题，靠着“知识库+向量+LLM”可以轻松解决
而实际情况可能是:
"搜索关于AI智能体相关的文章，文章发布时间在2025.09.05之前，发布者是新智元，返回3篇文章并给出文章的标题、链接、发布者"
这类请求融合了向量相似(文本语义)、关键词全文搜索、条件精确过滤、混合搜索等。问题的瓶颈不在LLM，更不是向量数据库。而是:
企业AI应用的大量场景从来都不是孤立的，更多的是与企业的业务紧密联系，这体现在应用、流程与数据多个层面

本项目为大家提供一种解决方案思路，以一个完整的闭环应用案例为大家介绍如何实现如下类似搜索:
"搜索关于AI智能体相关的文章，文章发布时间在2025.09.05之前，发布者是新智元，返回3篇文章并给出文章的标题、链接、发布者"
更甚者，存在无关条件信息的干扰，如:
搜索关于AI智能体相关的文章，文章发布时间在2025.09.05之前，发布者是新智元，字数在200字内，价格不超过500元，返回3篇文章并给出文章的标题、链接、发布者

主要实现的功能:
(1)使用低代码平台N8N实现对多个微信公众号文章进行自动采集并保存到本地文件
(2)使用主流的开源云原生向量数据库milvus将采集到的数据存储到知识库中并满足语义搜索、全文搜索及混合搜索
(3)将搜索功能封装为标准的MCP Server对外提供服务
(4)大模型(Agent)使用搜索MCP Server进行内容搜索
整体业务流程如下图所示:

2、前期准备工作

2.1 集成开发环境搭建

anaconda提供python虚拟环境,pycharm提供集成开发环境
具体参考如下视频:
【大模型应用开发-入门系列】03 集成开发环境搭建-开发前准备工作
https://youtu.be/KyfGduq5d7w

2.2 大模型LLM服务接口调用方案

(1)gpt大模型等国外大模型使用方案
国内无法直接访问，可以使用代理的方式，具体代理方案自己选择
这里推荐大家使用:https://nangeai.top/register?aff=Vxlp
(2)非gpt大模型方案 OneAPI方式或大模型厂商原生接口
(3)本地开源大模型方案(Ollama方式)
具体参考如下视频:
【大模型应用开发-入门系列】04 大模型LLM服务接口调用方案
https://youtu.be/mTrgVllUl7Y

3、项目初始化

3.1 下载源码

GitHub或Gitee中下载工程文件到本地，下载地址如下：
https://github.com/NanGePlus/RagWithMilvusTest
https://gitee.com/NanGePlus/RagWithMilvusTest

3.2 构建项目

使用pycharm构建一个项目，为项目配置虚拟python环境
项目名称：RagWithMilvusTest
虚拟环境名称保持与项目名称一致

3.3 将相关代码拷贝到项目工程中

将下载的代码文件夹中的文件全部拷贝到新建的项目根目录下

3.4 安装项目依赖

新建命令行终端，在终端中运行如下指令进行安装
pip install pymilvus==2.6.0
pip install openai==1.101.0
pip install mcp==1.8.0
pip install requests==2.32.5
注意: 建议先使用要求的对应版本进行本项目测试，避免因版本升级造成的代码不兼容。测试通过后，可进行升级测试

4、功能测试

详见各个部分的说明文档

Loading README…

Scored, not listed

Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

78/100across 5 weighted dimensions

How we score →

0255075100

−22

Security

Maintenance

Efficiency

Documentation

Compatibility

Categoriesclick a row to see evidence

Security

OSV.dev

No known CVEs.

Checked pymilvus against OSV.dev.

Inventory

Tools (1)

Click any tool to inspect its schema.

~173 tokens total

Community

Reviews

Be the first to review

Have you used this server?

Share your experience — it helps other developers decide.

How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is RagWithMilvusTest safe to use?: RagWithMilvusTest has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.
How do I install RagWithMilvusTest?: RagWithMilvusTest supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.
What can RagWithMilvusTest do?: RagWithMilvusTest provides 1 tool: search. See the full tools list on the server page for descriptions and parameters.
What AI clients work with RagWithMilvusTest?: RagWithMilvusTest is compatible with claude-desktop, cursor, claude-code. It uses stdio transport.
Is RagWithMilvusTest actively maintained?: RagWithMilvusTest is less actively maintained — last commit was 270 days ago. It has 43 GitHub stars.