ytyng · August 6, 2025 12:00
diff --git a/finder_agent.py b/finder_agent.py
 #!/usr/bin/env python3
 import asyncio
 import os

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

 app = MCPApp(name="hello_world_agent")


 async def example_usage():
    async with app.run() as mcp_agent_app:
        logger = mcp_agent_app.logger
        # This agent can read the filesystem or fetch URLs
        finder_agent = Agent(
            name="finder",
            instruction="""You can read local files or fetch URLs.
                Return the requested information when asked.""",
            server_names=[
                "fetch",
                "filesystem",
            ],  # MCP servers this Agent can use
        )

        async with finder_agent:
            # Automatically initializes the MCP servers and adds their tools for LLM use
            tools = await finder_agent.list_tools()
            logger.info(f"Tools available:", data=tools)

            # Attach an OpenAI LLM to the agent (defaults to GPT-4o)
            llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)

            # This will perform a file lookup and read using the filesystem server
            result = await llm.generate_str(
                message="Show me what's in README.md verbatim"
            )
            logger.info(f"README.md contents: {result}")

            # Uses the fetch server to fetch the content from URL
            result = await llm.generate_str(
                message="Print the first two paragraphs from https://www.anthropic.com/research/building-effective-agents"
            )
            logger.info(f"Blog intro: {result}")

            # Multi-turn interactions by default
            result = await llm.generate_str(
                "Summarize that in a 128-char tweet"
            )
            logger.info(f"Tweet: {result}")


 if __name__ == "__main__":
    asyncio.run(example_usage())
diff --git a/mcp-agent-setup-guide.md b/mcp-agent-setup-guide.md
diff --git a/mcp_agent.config.yaml b/mcp_agent.config.yaml
 execution_engine: asyncio
 logger:
  transports: [console] # You can use [file, console] for both
  level: debug
  path: "logs/mcp-agent.jsonl" # Used for file transport
  # For dynamic log filenames:
  # path_settings:
  #   path_pattern: "logs/mcp-agent-{unique_id}.jsonl"
  #   unique_id: "timestamp"  # Or "session_id"
  #   timestamp_format: "%Y%m%d_%H%M%S"

 mcp:
  servers:
    fetch:
      command: "uvx"
      args: ["mcp-server-fetch"]
    filesystem:
      command: "npx"
      args:
        [
          "-y",
          "@modelcontextprotocol/server-filesystem",
          "/Users/ytyng/workspace",  # ← ここを実際のパスに変更
        ]

 openai:
  # Secrets (API keys, etc.) are stored in an mcp_agent.secrets.yaml file which can be gitignored
  default_model: gpt-4o-mini  # ← 正しいモデル名に修正
	#!/usr/bin/env python3
	import asyncio
	import os

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

	app = MCPApp(name="hello_world_agent")


	async def example_usage():
	async with app.run() as mcp_agent_app:
	logger = mcp_agent_app.logger
	# This agent can read the filesystem or fetch URLs
	finder_agent = Agent(
	name="finder",
	instruction="""You can read local files or fetch URLs.
	Return the requested information when asked.""",
	server_names=[
	"fetch",
	"filesystem",
	], # MCP servers this Agent can use
	)

	async with finder_agent:
	# Automatically initializes the MCP servers and adds their tools for LLM use
	tools = await finder_agent.list_tools()
	logger.info(f"Tools available:", data=tools)

	# Attach an OpenAI LLM to the agent (defaults to GPT-4o)
	llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)

	# This will perform a file lookup and read using the filesystem server
	result = await llm.generate_str(
	message="Show me what's in README.md verbatim"
	)
	logger.info(f"README.md contents: {result}")

	# Uses the fetch server to fetch the content from URL
	result = await llm.generate_str(
	message="Print the first two paragraphs from https://www.anthropic.com/research/building-effective-agents"
	)
	logger.info(f"Blog intro: {result}")

	# Multi-turn interactions by default
	result = await llm.generate_str(
	"Summarize that in a 128-char tweet"
	)
	logger.info(f"Tweet: {result}")


	if __name__ == "__main__":
	asyncio.run(example_usage())
	execution_engine: asyncio
	logger:
	transports: [console] # You can use [file, console] for both
	level: debug
	path: "logs/mcp-agent.jsonl" # Used for file transport
	# For dynamic log filenames:
	# path_settings:
	# path_pattern: "logs/mcp-agent-{unique_id}.jsonl"
	# unique_id: "timestamp" # Or "session_id"
	# timestamp_format: "%Y%m%d_%H%M%S"

	mcp:
	servers:
	fetch:
	command: "uvx"
	args: ["mcp-server-fetch"]
	filesystem:
	command: "npx"
	args:
	[
	"-y",
	"@modelcontextprotocol/server-filesystem",
	"/Users/ytyng/workspace", # ← ここを実際のパスに変更
	]

	openai:
	# Secrets (API keys, etc.) are stored in an mcp_agent.secrets.yaml file which can be gitignored
	default_model: gpt-4o-mini # ← 正しいモデル名に修正