Stata-MCP

#!/usr/bin/python3 # -*- coding: utf-8 -*- # # Copyright (C) 2025 - Present Sepine Tam, Inc. All Rights Reserved # # @Author : Sepine Tam (谭淞) # @Email : sepinetam@gmail.com # @File : main.py import os import asyncio from time import perf_counter from openai import AsyncOpenAI from agents import Agent, Runner, OpenAIChatCompletionsModel, set_tracing_disabled from agents.mcp import MCPServerStdio # If you are using Cloudflare AI Gateway to store the agent chat histories, you should disable the tracing. # set_tracing_disabled(True) mcp_server = MCPServerStdio( name="Stata-MCP", params={ "command": "uvx", "args": [ "stata-mcp" # or you can use the local beta version with the git clone repo. ], "env": { "stata_cli": "stata-mp" # alternative you can change your Stata path here } } ) # It looks like the system prompt, you can change or add more information to instructions. # I always figure that better instructions, better performance. # Here is just an example of instructions, in the real task you should use a better one. model_instructions = """ You are a helpful economics agent, you can help user to achieve their task with tools """ agent = Agent( name="Econ Assistant", instructions=model_instructions, mcp_servers=[mcp_server], # If you hope to add other MCP servers, you can add them here. model=OpenAIChatCompletionsModel( # If you have set environment of OPENAI_API_KEY, you can ignore arg `model`. model="deepseek-chat", # As I am located in China, I use DeepSeek as model provider, you can change to OpenAI. openai_client=AsyncOpenAI( base_url=os.getenv("OPENAI_BASE_URL", "https://api.deepseek.com/v1"), # "https://api.openai.com/v1" api_key=os.getenv("OPENAI_API_KEY") # Suggest saving your API KEY in environment variables. IMPORTANT! ) ), ) async def main(msg: str): await mcp_server.connect() start = perf_counter() try: result = await Runner.run(agent, msg, max_turns=30) print(result.final_output) finally: elapsed = perf_counter() - start print(f"Total cost time: {elapsed:.2f} s") await mcp_server.cleanup() return result # This task message is also an example, there is no research meaning. task_message = """ Using the Stata default data, analysis the relationship between `mpg` and `price`, moreover add `weight` as a control. """ if __name__ == "__main__": asyncio.run(main(msg=task_message))