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Py2755/aiogram-mcp

by Various

MCP server middleware for aiogram Telegram bots — expose your bot to AI agents via the Model Context Protocol

P

MCP

Py2755/aiogram-mcp

Added 1 June 2026

#aiogram #bot #claude #mcp #model-context-protocol #python #telegram

Overview

Py2755/aiogram-mcp is middleware that adds a Model Context Protocol (MCP) server to aiogram Telegram bots, enabling AI agents to interact with the bot through a standardized interface. It translates between the aiogram event loop and MCP requests, allowing agents to send messages and trigger bot handlers.

Best for

Best for
Developers who already use aiogram and want to make their Telegram bot accessible to MCP-based AI agents.

Use cases

  • Connect an existing aiogram Telegram bot to MCP-compatible AI agents
  • Expose bot commands and handlers as tools for LLM orchestration
  • Build a bridge between Telegram user interactions and agent workflows

Notes

Py2755/aiogram-mcp is middleware that adds a Model Context Protocol (MCP) server to aiogram Telegram bots, enabling AI agents to interact with the bot through a standardized interface. It translates between the aiogram event loop and MCP requests, allowing agents to send messages and trigger bot handlers.

0 stars on GitHub. Last updated 2026-03-16. Licensed MIT.

Use cases

  • Connect an existing aiogram Telegram bot to MCP-compatible AI agents
  • Expose bot commands and handlers as tools for LLM orchestration
  • Build a bridge between Telegram user interactions and agent workflows

Pros

  • Leverages the open MCP standard for interoperability with multiple agent frameworks
  • Written in Python with minimal overhead for aiogram projects
  • Lightweight middleware that does not require rewriting bot logic

Cons

  • GitHub stars at zero indicating very early stage and lack of community validation
  • Limited documentation or examples beyond the repository itself
  • Dependent on aiogram and MCP library versions which may shift

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Leverages the open MCP standard for interoperability with multiple agent frameworks
  • Written in Python with minimal overhead for aiogram projects
  • Lightweight middleware that does not require rewriting bot logic

Cons

  • GitHub stars at zero indicating very early stage and lack of community validation
  • Limited documentation or examples beyond the repository itself
  • Dependent on aiogram and MCP library versions which may shift