LINE Bot MCP Server
Enables AI agents to send messages, manage rich menus, and interact with users through LINE Official Accounts via the LINE Messaging API. Supports both individual messaging and broadcasting to all followers with text and customizable flex messages.
README
LINE Bot MCP Server
Model Context Protocol (MCP) server implementation that integrates the LINE Messaging API to connect an AI Agent to the LINE Official Account.

[!NOTE] This repository is provided as a preview version. While we offer it for experimental purposes, please be aware that it may not include complete functionality or comprehensive support.
Tools
- push_text_message
- Push a simple text message to a user via LINE.
- Inputs:
user_id(string?): The user ID to receive a message. Defaults to DESTINATION_USER_ID. Eitheruser_idorDESTINATION_USER_IDmust be set.message.text(string): The plain text content to send to the user.
- push_flex_message
- Push a highly customizable flex message to a user via LINE.
- Inputs:
user_id(string?): The user ID to receive a message. Defaults to DESTINATION_USER_ID. Eitheruser_idorDESTINATION_USER_IDmust be set.message.altText(string): Alternative text shown when flex message cannot be displayed.message.content(any): The content of the flex message. This is a JSON object that defines the layout and components of the message.message.contents.type(enum): Type of the container. 'bubble' for single container, 'carousel' for multiple swipeable bubbles.
- broadcast_text_message
- Broadcast a simple text message via LINE to all users who have followed your LINE Official Account.
- Inputs:
message.text(string): The plain text content to send to the users.
- broadcast_flex_message
- Broadcast a highly customizable flex message via LINE to all users who have added your LINE Official Account.
- Inputs:
message.altText(string): Alternative text shown when flex message cannot be displayed.message.content(any): The content of the flex message. This is a JSON object that defines the layout and components of the message.message.contents.type(enum): Type of the container. 'bubble' for single container, 'carousel' for multiple swipeable bubbles.
- get_profile
- Get detailed profile information of a LINE user including display name, profile picture URL, status message and language.
- Inputs:
user_id(string?): The ID of the user whose profile you want to retrieve. Defaults to DESTINATION_USER_ID.
- get_message_quota
- Get the message quota and consumption of the LINE Official Account. This shows the monthly message limit and current usage.
- Inputs:
- None
- get_rich_menu_list
- Get the list of rich menus associated with your LINE Official Account.
- Inputs:
- None
- delete_rich_menu
- Delete a rich menu from your LINE Official Account.
- Inputs:
richMenuId(string): The ID of the rich menu to delete.
- set_rich_menu_default
- Set a rich menu as the default rich menu.
- Inputs:
richMenuId(string): The ID of the rich menu to set as default.
- cancel_rich_menu_default
- Cancel the default rich menu.
- Inputs:
- None
Installation (Using npx)
requirements:
- Node.js v20 or later
Step 1: Create LINE Official Account
This MCP server utilizes a LINE Official Account. If you do not have one, please create it by following this instructions.
If you have a LINE Official Account, enable the Messaging API for your LINE Official Account by following this instructions.
Step 2: Configure AI Agent
Please add the following configuration for an AI Agent like Claude Desktop or Cline.
Set the environment variables or arguments as follows:
CHANNEL_ACCESS_TOKEN: (required) Channel Access Token. You can confirm this by following this instructions.DESTINATION_USER_ID: (optional) The default user ID of the recipient. If the Tool's input does not includeuser_id,DESTINATION_USER_IDis required. You can confirm this by following this instructions.
{
"mcpServers": {
"line-bot": {
"command": "npx",
"args": [
"@line/line-bot-mcp-server"
],
"env": {
"CHANNEL_ACCESS_TOKEN" : "FILL_HERE",
"DESTINATION_USER_ID" : "FILL_HERE"
}
}
}
}
Installation (Using Docker)
Step 1: Create LINE Official Account
This MCP server utilizes a LINE Official Account. If you do not have one, please create it by following this instructions.
If you have a LINE Official Account, enable the Messaging API for your LINE Official Account by following this instructions.
Step 2: Build line-bot-mcp-server image
Clone this repository:
git clone git@github.com:line/line-bot-mcp-server.git
Build the Docker image:
docker build -t line/line-bot-mcp-server .
Step 3: Configure AI Agent
Please add the following configuration for an AI Agent like Claude Desktop or Cline.
Set the environment variables or arguments as follows:
mcpServers.args: (required) The path toline-bot-mcp-server.CHANNEL_ACCESS_TOKEN: (required) Channel Access Token. You can confirm this by following this instructions.DESTINATION_USER_ID: (optional) The default user ID of the recipient. If the Tool's input does not includeuser_id,DESTINATION_USER_IDis required. You can confirm this by following this instructions.
{
"mcpServers": {
"line-bot": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"CHANNEL_ACCESS_TOKEN",
"-e",
"DESTINATION_USER_ID",
"line/line-bot-mcp-server"
],
"env": {
"CHANNEL_ACCESS_TOKEN" : "FILL_HERE",
"DESTINATION_USER_ID" : "FILL_HERE"
}
}
}
}
Local Development with Inspector
You can use the MCP Inspector to test and debug the server locally.
Prerequisites
- Clone the repository:
git clone git@github.com:line/line-bot-mcp-server.git
cd line-bot-mcp-server
- Install dependencies:
npm install
- Build the project:
npm run build
Run the Inspector
After building the project, you can start the MCP Inspector:
npx @modelcontextprotocol/inspector node dist/index.js
This will start the MCP Inspector interface where you can interact with the LINE Bot MCP Server tools and test their functionality.
Versioning
This project respects semantic versioning
See http://semver.org/
Contributing
Please check CONTRIBUTING before making a contribution.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。