What MCP transports are demonstrated?
Local tools use StdioServerParams (standard input/output), and remote tools use SseServerParams (Server‑Sent Events).
@SaM-92
基本情報
This repository demonstrates how to use AutoGen to integrate local and remote MCP (Model Context Protocol) servers. It showcases a local math tool (math_server.py) using Stdio and a remote Apify tool (RAG Web Browser Actor) via SSE for tasks like arithmetic and web browsing.
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_autogen_sse_stdio": {
"command": "uv",
"args": [
"venv",
"--python",
"3.12"
]
}
}
}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。
This repository demonstrates how to connect AI agents to tools using the Model Context Protocol (MCP) within the AutoGen framework. It shows integration with both a local tool server (Stdio transport) and a remote tool server (SSE transport) to solve real queries.
Set up a Python 3.12 virtual environment with uv, install dependencies via uv pip install -e ., and create a .env file with your OPENAI_API_KEY and APIFY_API_KEY. Run the demo from the parent directory using uv run mcp_autogen_sse_stdio/main.py.
add, multiply)AssistantAgent configured with both tool setsLocal tools use StdioServerParams (standard input/output), and remote tools use SseServerParams (Server‑Sent Events).
Python 3.12, the `uv` package manager, and API keys for OpenAI and Apify.
Create a `.env` file in the `mcp_autogen_sse_stdio` directory and add `OPENAI_API_KEY` and `APIFY_API_KEY`. The Apify key can be obtained from the Apify MCP Server page.
The framework generalizes to other MCP servers that support Stdio or SSE transports, though only the Apify remote server is shown.
Yes, the repository includes an animated GIF (`images/mcp_exmpl.gif`) illustrating the workflow.
A code reasoning MCP server, a fork of sequential-thinking
A comprehensive Model Context Protocol (MCP) server providing AI assistants with powerful task management and agent memories capabilities with project-specific storage.
ArduPilotドローンをAIエージェントから操作するMCPサーバーです。
An MCP server allowing AI assistants to use a Neo4j knowledge graph as their primary, dynamic instruction manual and long term project memory with adaptive templating and autonomous tool development tools.
Multi-Agent AI Orchestration Workshop
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