agent_use_mcp

# Tutorial - MCP样例 By Python ### 注意： 生产环境，请参考目录：qwen_agent 以下开始，更偏向于技术原理角度 ### 环境准备 #### 1. 需要从github上找到这几个代码库 ```bash # mcp-agent： 这个是主app用的框架，用它来实现query llm及bridge to mcp，也就是找excel读excel的操作都在它这里完成 git clone https://github.com/lastmile-ai/mcp-agent pip install -e . # 或者也可以直接pip install mcp-agent # pandas mcp：这个用来提供读取及分析excel的pandas服务 git clone https://github.com/marlonluo2018/pandas-mcp-server.git # 别忘了pip install pandas，并且按装读取excel的三方库：pip install openpyxl # file system mcp：这个用来对local directory做文件查找 git clone https://github.com/ad/mcp-filesystem.git go build -o mcp-fs main.go ``` 其中mcp-filesystem要改一下server.py，找到如下的方法修改mcp.run这行 ```python def main(): try: if not init_logging(): raise RuntimeError("Failed to initialize logging") logger.debug("Starting stdio MCP server...") mcp.run(transport="sse") ## 修改这行 except Exception as e: logger.error(f"Server failed to start: {str(e)}") logger.debug(traceback.format_exc()) raise ``` #### 2. 接下来实现我们自己的python app程序 ```python 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 from mcp_agent.workflows.llm.augmented_llm_google import GoogleAugmentedLLM 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=["filesystem", "pandas"], # 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(GoogleAugmentedLLM) # This will perform a file lookup and read using the filesystem server result = await llm.generate_str( # 这里的Prompt按你自己的改 message="list all excel names in '/data2/working/pandas-mcp-server/data'." ) logger.info(f"found: {result}") result = await llm.generate_str(                 # 这里的Prompt按你自己的改 message="""then rename all columns' name to lower case. then find the mean length of the column smiles if any. output as filename: smiles_mean_length""" ) logger.info(f"calculate mean length: {result}") if __name__ == "__main__": asyncio.run(example_usage()) ``` ### 运行准备 #### 1. 启动filesystem mcp server ```bash # 最后那个是允许读写的目录 cd mcp-filesystem ./mcp-fs -transport http -port 8989 /data2/working/pandas-mcp-server/data 2025/07/22 03:23:52 Starting MCP server with streamable HTTP transport on port 8989... 2025/07/22 03:23:52 HTTP server listening on :8989/mcp ``` #### 2. 启动pandas mcp server ```bash cd pandas-mcp-server python server.py [07/22/25 03:25:06] INFO Logging configured with single log file: /data2/working/pandas-mcp-server/logs/mcp_server.log server.py:60 INFO Log file created with permissions: 644 server.py:65 INFO: Started server process [183973] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit) ``` ## 运行APP 它将请求LLM及MCP，得到结果 ```bash cd app python demo.py # ...... # 以上省略屏幕输出 cat results.log The directory '/data2/working/pandas-mcp-server/data' contains the following Excel files: * chembl.xlsx * test_96cpds.xlsx /data2/working/pandas-mcp-server/data/test_96cpds.xlsx: 20.541666666666668 /data2/working/pandas-mcp-server/data/chembl.xlsx: 63.76939203354298 # 注意这里有时结果不对的，要核对 ```