Microsoft Agent Framework

pip install agent-framework mcp python-dotenv

import asyncio
import os
from pathlib import Path

from agent_framework import ChatAgent, MCPStreamableHTTPTool
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv


async def main():
    load_dotenv()

    mcp_tool = MCPStreamableHTTPTool(
        name="twexapi",
        url="https://api.twexapi.io/mcp",
        headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
        description="Twexapi X/Twitter API tools exposed through MCP",
    )

    async with mcp_tool:
        agent = ChatAgent(
            chat_client=OpenAIChatClient(model_id="gpt-4o"),
            name="twexapi_agent",
            instructions=(
                "You help users interact with X/Twitter via Twexapi. "
                "Call explore first, then twexapi_request. Preserve IDs and cursors."
            ),
            tools=[mcp_tool],
        )

        response = await agent.run(
            "Search for tweets about AI agents. Return compact JSON with "
            "route_used, tweets[{tweet_id,text,author_username,created_at}], "
            "has_more, and next_cursor."
        )

        Path("twexapi-agent-framework-handoff.json").write_text(
            response.text,
            encoding="utf-8",
        )


asyncio.run(main())

{
  "name": "twexapi",
  "transport": "streamable-http",
  "url": "https://api.twexapi.io/mcp",
  "headers": {
    "x-api-key": "twexapi_YOUR_KEY_HERE"
  }
}

You have access to Twexapi MCP tools.
Call explore before twexapi_request.
Use only relative paths returned by explore.
Return tweet_id, user_id, author_username, route_used, has_more, and next_cursor.
Ask for confirmation before read_only: false actions.

Research current X/Twitter discussion about "AI coding agents".
Use Twexapi MCP, collect 25 recent tweets, group them by theme, and return compact JSON.
Include tweet_id, author_username, text, created_at, engagement metrics, route_used, has_more, and next_cursor.