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Ai2flomo
What is Ai2flomo
ai2flomo is a Model Context Protocol (MCP) service built using the FastMCP framework. It allows AI assistants, such as Claude and Cherry Studio, to send notes to Flomo and query historical sending records. This service acts as a bridge between AI assistants and the Flomo note-taking application, enabling quick recording of ideas and inspirations through conversational interaction.
Use cases
Use cases for ai2flomo include recording meeting notes, jotting down creative ideas, organizing tasks with tags, and retrieving past notes for reference, all through a seamless conversational interface.
How to use
To use ai2flomo, first install the necessary dependencies and configure the Flomo Webhook URL in the ‘config.py’ file. Then, start the service using either the recommended Stdio mode or the advanced SSE mode. Finally, configure your AI assistant (like Claude or Cherry Studio) to connect to the ai2flomo service by providing the required settings.
Key features
Key features of ai2flomo include: sending notes directly to Flomo via conversation, tag support for organizing notes, historical record querying by content or tag, and automatic synchronization of notes to both Flomo and local storage for backup.
Where to use
ai2flomo can be used in various fields such as personal productivity, project management, and creative writing, where quick note-taking and organization of thoughts are essential.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Ai2flomo
ai2flomo is a Model Context Protocol (MCP) service built using the FastMCP framework. It allows AI assistants, such as Claude and Cherry Studio, to send notes to Flomo and query historical sending records. This service acts as a bridge between AI assistants and the Flomo note-taking application, enabling quick recording of ideas and inspirations through conversational interaction.
Use cases
Use cases for ai2flomo include recording meeting notes, jotting down creative ideas, organizing tasks with tags, and retrieving past notes for reference, all through a seamless conversational interface.
How to use
To use ai2flomo, first install the necessary dependencies and configure the Flomo Webhook URL in the ‘config.py’ file. Then, start the service using either the recommended Stdio mode or the advanced SSE mode. Finally, configure your AI assistant (like Claude or Cherry Studio) to connect to the ai2flomo service by providing the required settings.
Key features
Key features of ai2flomo include: sending notes directly to Flomo via conversation, tag support for organizing notes, historical record querying by content or tag, and automatic synchronization of notes to both Flomo and local storage for backup.
Where to use
ai2flomo can be used in various fields such as personal productivity, project management, and creative writing, where quick note-taking and organization of thoughts are essential.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Flomo笔记 MCP 服务
这是一个使用FastMCP框架构建的 MCP 服务,可以让AI助手(如Claude, Cherry Studio等)将你想要的信息发送到Flomo,并查询历史发送记录。这个服务充当AI助手与Flomo笔记应用之间的桥梁,让你能够通过对话方式快速记录灵感和想法。
功能概述
- 发送笔记到Flomo:通过对话直接发送笔记内容到你的Flomo账户
- 标签支持:添加标签进行分类整理,自动转换为Flomo的#tag格式
- 历史记录查询:可按内容或标签搜索历史发送记录
- 自动同步:笔记同时保存到Flomo和本地,实现双向备份
快速开始
1. 安装准备
系统要求:
- Python 3.8+
- pip包管理器
- 有效的Flomo Webhook URL
安装步骤:
- 克隆或下载本项目到本地
- 安装必要依赖:
pip install -r requirements.txt - 配置Flomo Webhook URL(见下文配置部分)
2. 配置服务
打开config.py文件,根据需要修改以下配置项:
# Flomo API配置 - 必须替换为你自己的Webhook URL
FLOMO_WEBHOOK_URL = "https://flomoapp.com/iwh/你的ID/你的WEBHOOK_KEY/"
# 数据存储路径 - 通常无需修改
DATA_DIR = "data" # 数据存储目录
NOTES_FILE = os.path.join(DATA_DIR, "notes.json") # 历史记录文件名
# 默认标签 - 可以添加你常用的标签
DEFAULT_TAGS = [] # 例如 ["重要", "工作", "学习"]
# SSE服务配置 - 如果端口被占用可以修改
SSE_PORT = 3000 # 默认端口
SSE_HOST = "0.0.0.0" # 监听地址
配置Flomo Webhook URL的步骤:
- 登录你的Flomo账户
- 访问API页面:https://flomoapp.com/mine?source=incoming_webhook
- 点击"添加新的Webhook"
- 复制生成的URL
- 将该URL粘贴到配置文件的
FLOMO_WEBHOOK_URL处
3. 启动服务
有两种启动方式,根据需求选择:
方法1:Stdio模式(推荐)
./start_flomo.sh
方法2:SSE模式(高级用途)
./start_flomo.sh --sse --port 3000
4. 在AI助手中配置
Claude桌面版配置方法
- 打开Claude桌面应用
- 打开setting -> Developer
- 点击选项"Edit Config"
- 进入 Claude 目录打开文件"claude_desktop_config.json"
- 填写以下信息:
{
"mcpServers": {
"ai2flomo": {
"name": "Flomo笔记服务",
"type": "stdio",
"description": "一个用于添加和管理笔记的工具,支持与Flomo同步",
"isActive": true,
"command": "/Users/username/workspace/mcp_space/ai2flomo/start_flomo.sh",
"args": [],
"env": {
"PATH": "/opt/miniconda3/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin"
}
}
}
}
- 替换"start_flomo.sh"文件的路径与环境变量"env"为你的电脑设置。
- 点击"保存"
Cherry Studio配置方法
- 打开Cherry Studio应用
- 点击"设置"图标
- 在左侧菜单中选择"MCP服务器"
- 点击"添加服务器"按钮
- 填写以下信息:
- 名称:Flomo笔记服务
- 描述:通过AI助手发送笔记到Flomo并管理历史记录
- 类型:标准输入/输出(stdio)
- 命令:填写
start_flomo.sh脚本的完整路径 - 环境变量:可填写你系统的环境变量
- 点击"保存"
- 点击"保存"按钮左侧的开关,确认服务能正常连接
Cherry Studio的JSON配置示例:
在Cherry Studio中,你也可以直接编辑JSON配置。点击"编辑JSON"按钮,可以看到类似以下的配置:
{
"mcpServers": {
"MDzXVj9bhliofwJ8eCHs9": {
"name": "Flomo笔记服务",
"type": "stdio",
"description": "一个用于添加和管理笔记的工具,支持与Flomo同步",
"isActive": true,
"command": "/Users/username/workspace/mcp_space/ai2flomo/start_flomo.sh",
"args": [],
"env": {
"PATH": "/opt/miniconda3/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin"
}
}
}
}
确保将路径/Users/username/workspace/mcp_space/ai2flomo/start_flomo.sh替换为你系统上实际的start_flomo.sh脚本路径。
如果你使用SSE模式,配置方式略有不同:
- 选择"类型"为"SSE"
- 在"URL"字段中填入:
http://localhost:3000/sse(如果修改了端口号,请相应调整) - 其他步骤与上述相同
使用指南
当服务启动并在AI助手中配置完成后,你可以通过以下方式使用:
发送笔记到Flomo
简单地告诉AI助手你想记录的内容:
- “将’这个想法很有意思,我应该进一步研究’发送到Flomo”
- “帮我添加笔记:‘今天学习了MCP协议的基础知识’,标签为’学习 编程’”
搜索历史记录
你可以按内容或标签搜索之前发送过的笔记:
- “查找包含’Python’的历史发送记录”
- “显示所有带’学习’标签的历史发送记录”
- “列出所有历史发送记录的标签”
- “展示我最近发送的5条记录”
项目结构
. ├── app.py # 主程序文件,包含所有MCP工具和资源定义 ├── config.py # 配置文件,包含API密钥、路径和其他设置 ├── start_flomo.sh # 启动脚本 ├── requirements.txt # 项目依赖 ├── data/ # 数据存储目录 │ └── notes.json # 历史发送记录数据文件 ├── logs/ # 日志目录 └── README.md # 项目文档
技术细节
API与工具
服务提供以下MCP工具和资源:
工具 (Tools)
-
send_to_flomo(content, tags)
- 将笔记发送到Flomo并保存到本地
- 参数:
content: 笔记内容tags: 标签,多个标签用空格分隔
-
search_notes(query, tag)
- 搜索历史发送记录
- 参数:
query: 内容搜索关键字tag: 标签搜索关键字
-
get_all_tags()
- 获取所有历史发送记录中使用的标签列表
资源 (Resources)
-
notes://recent
- 获取最近添加的5条历史发送记录
-
notes://{note_id}
- 通过ID获取特定历史发送记录
数据存储
服务使用简单的JSON文件作为存储,位于data/notes.json。每条记录包含:
- 唯一ID
- 内容
- 标签列表
- 创建和更新时间戳
标签处理
- 输入格式:标签以空格分隔,如
"学习 编程 Python" - Flomo格式:自动转换为Flomo的#tag格式,如
#学习 #编程 #Python - 默认标签:如果在配置文件中设置了
DEFAULT_TAGS,这些标签会自动添加到每条笔记中
常见问题解答
Q: 我的Flomo Webhook URL在哪里可以找到?
A: 登录Flomo网站,访问https://flomoapp.com/mine?source=incoming_webhook 即可查看和创建。
Q: 发送笔记失败怎么办?
A: 检查你的Webhook URL是否正确,以及网络连接是否正常。错误信息会在服务日志中显示。
Q: 如何添加默认标签?
A: 编辑config.py文件,修改DEFAULT_TAGS列表,如DEFAULT_TAGS = ["日常", "AI助手"]。
Q: 如何备份历史发送记录?
A: 所有记录都保存在data/notes.json文件中,可以定期备份此文件。
关于MCP
Model Context Protocol (MCP) 是一种标准化协议,允许AI模型安全地与外部工具和服务进行交互。通过MCP,AI助手可以:
- 使用工具(Tools) - 执行操作,如发送笔记到Flomo
- 访问资源(Resources) - 获取信息,如历史发送记录
- 使用提示模板(Prompts) - 按照预定义模式进行交互
参考资源
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.










