About Telegram MCP Server
Telegram MCP Server lets AI assistants send messages to a configured Telegram chat. It is intended for MCP-compatible clients such as Claude Desktop and provides a single message-sending tool.
Key features
- 01Send a message to a configured Telegram chat
Use cases
- 01Send assistant-generated updates to a Telegram chat
MCP support
It exposes the `send_telegram_message` tool to MCP clients, using a configured Telegram Bot API token and chat ID.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMETelegram MCP Server
This project implements a Model Context Protocol (MCP) server that enables sending Telegram messages through AI assistants like Claude Desktop.
What is MCP?
The Model Context Protocol (MCP) is a system that lets AI apps, like Claude Desktop, connect to external tools and data sources. It gives a clear and safe way for AI assistants to work with local services and APIs while keeping the user in control.
Setup
- Install dependencies:
npm install
- Build the project:
npm run build
Configuration
The server requires two environment variables to be set:
TELEGRAM_TOKEN: Your Telegram Bot API tokenCHAT_ID: The ID of the Telegram chat where messages will be sent
These can be configured in your .cursor/mcp.json file:
{
"mcpServers": {
"telegram": {
"command": "node",
"args": ["./dist/mcp-server.js"],
"env": {
"TELEGRAM_TOKEN": "your-telegram-bot-token",
"CHAT_ID": "your-chat-id"
}
}
}
}
Available Tools
send_telegram_message
Sends a message to the configured Telegram chat.
Parameters:
message(string): The message text to send
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.