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MCP Integration example with Telegram

@arananet

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基本情報

カテゴリ

コミュニケーション

ライセンス

MIT license

トランスポート

stdio

作者

arananet

MCP Integration example with Telegram について

A Python-based Model Context Protocol (MCP) client and server that enables LLMs to send notifications via Telegram and receive user responses.

MCP Integration example with Telegram の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-telegram-poc": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

MCP Integration example with Telegram のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP Integration example with Telegram?

A Python-based Model Context Protocol (MCP) client and server that enables LLMs to send notifications via Telegram and receive user responses. It decouples the LLM’s reasoning from the execution of external actions, making tool integration more modular and maintainable.

How to use MCP Integration example with Telegram?

Clone the repository, install dependencies (pip install -r requirements.txt), create a virtual environment, and fill in your Telegram credentials in a .env file (Telegram bot token, chat ID, OpenAI API key). Run the server with python3 src/mcp_handler.py, or test client interaction with python3 examples/test_client.py. The server exposes two tools: send_alert (sends a Telegram message) and check_reply (polls for replies).

Key features of MCP Integration example with Telegram

  • Send text alerts to Telegram with customizable urgency levels
  • Check for user replies with configurable timeouts
  • Integration with MCP‑compatible LLM applications
  • Decouples reasoning from execution for safer tool use
  • Update external tools without changing LLM prompts
  • Supports AutoGen 0.4 framework integration

Use cases of MCP Integration example with Telegram

  • Notify a user of critical events and wait for confirmation
  • Enable an LLM to send alerts and process human feedback
  • Prototype MCP‑based tool integrations with messaging platforms
  • Demonstrate modular architecture for LLM‑driven workflows

よくある質問

1

How does MCP differ from function calls or agent tools?

MCP separates the LLM’s decision‑making from execution; you can swap out tools without modifying the LLM. Direct function calls or agent methods mix logic, making maintenance harder.

2

What are the prerequisites?

Python 3.11+, a Telegram bot token (from @BotFather), your Telegram chat ID, and an OpenAI API key.

3

How do I get a Telegram bot token and find my chat ID?

Chat with @BotFather on Telegram, send `/newbot`, and follow the prompts. To find your chat ID, send a message to your bot, then visit `https://api.telegram.org/bot<YOUR_BOT_TOKEN>/getUpdates` and look for the `chat` object’s `id` field.

4

What runtime environment is needed?

The server runs as a standalone Python script. Use a virtual environment and install dependencies from `requirements.txt`. It is tested on Linux/macOS.

5

Are there known limits?

The README does not specify limits beyond the configurable `timeout_seconds` for `check_reply` (default 60 seconds). The server uses polling for replies.

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