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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.