Quick Start

import asyncio
from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM

# Create the MCP app

app = MCPApp(name="finder_agent")

async def main():
    async with app.run() as mcp_agent_app:
        logger = mcp_agent_app.logger

        # Create an agent with access to fetch and filesystem servers
        finder_agent = Agent(
            name="finder",
            instruction="""You can read local files or fetch URLs.
                Return the requested information when asked.""",
            server_names=["fetch", "filesystem"]
        )

        async with finder_agent:
            # List available tools
            list_tools_result = await finder_agent.list_tools()
            logger.info("Available tools:", data=[tool.name for tool in list_tools_result.tools])

            # Attach an OpenAI LLM to the agent
            llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)

            # Example 1: Read a local file
            print("\n🔍 Reading local file...")
            result = await llm.generate_str(
                "Create a simple README.md file, then show me its contents"
            )
            print(f"📄 Result: {result}")

            # Example 2: Fetch web content
            print("\n🌐 Fetching web content...")
            result = await llm.generate_str(
                "Fetch the first two paragraphs from https://www.anthropic.com/research/building-effective-agents"
            )
            print(f"📰 Result: {result}")

            # Example 3: Multi-turn conversation
            print("\n💬 Multi-turn conversation...")
            result = await llm.generate_str(
                "Summarize that content in a 140-character tweet"
            )
            print(f"🐦 Tweet: {result}")

if __name__ == "__main__":
    asyncio.run(main())