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 : agent_langchain.py import os import asyncio from time import perf_counter from typing import Dict # We suppose you have set the api_base and api_key. # Note: The environment name is "OPENAI_API_BASE" and "OPENAI_API_KEY" # os.environ["OPENAI_API_BASE"] = os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com/v1") # os.environ["OPENAI_API_KEY"] = os.getenv("DEEPSEEK_API_KEY") from langchain.chat_models import init_chat_model from langchain_mcp_adapters.client import MultiServerMCPClient from langgraph.prebuilt import create_react_agent # langgraph is more stable than langchain react agent from prompt_generator import PromptGenerator generator = PromptGenerator(template_name="ReAct", language="English", agent_provider="langchain") model_instructions = generator.instructions() model_tasks = generator.tasks( datas="The default data of Stata", aims="Get the data structure and know the relationship between mpg and price", ) # You should set the stata_cli here, not in the client args os.environ["stata_cli"] = "stata-mp" client = MultiServerMCPClient( { "stata-mcp": { "command": "uvx", "args": ['stata-mcp'], "transport": "stdio", } } ) # If you are setting other providers, you can set whatever model you want, visit official documents to change it. model = init_chat_model(model="deepseek-chat", model_provider="openai") async def main(msg: Dict[str, str]): tools = await client.get_tools() start = perf_counter() agent = create_react_agent(model, tools, prompt=model_instructions) result = await agent.ainvoke(msg) elapsed = perf_counter() - start print(result) print(f"Total cost time: {elapsed:.2f} s") return result task_messages = {"messages": model_tasks} if __name__ == "__main__": asyncio.run(main(task_messages))