Как создать OpenAI-агент с помощью MCP-сервера ClickHouse.

pip install -q --upgrade pip
pip install -q openai-agents

import os, getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter OpenAI API Key:")

from agents.mcp import MCPServer, MCPServerStdio
from agents import Agent, Runner, trace
import json

def simple_render_chunk(chunk):
    """Simple version that just filters important events"""

    # Вызовы инструментов
    if (hasattr(chunk, 'type') and
            chunk.type == 'run_item_stream_event'):

        if chunk.name == 'tool_called':
            tool_name = chunk.item.raw_item.name
            args = chunk.item.raw_item.arguments
            print(f"🔧 Tool: {tool_name}({args})")

        elif chunk.name == 'tool_output':
            try:
                # Обработка строкового и уже разобранного вывода
                if isinstance(chunk.item.output, str):
                    output = json.loads(chunk.item.output)
                else:
                    output = chunk.item.output

                # Обработка форматов dict и list
                if isinstance(output, dict):
                    if output.get('type') == 'text':
                        text = output['text']
                        if 'Error' in text:
                            print(f"❌ Error: {text}")
                        else:
                            print(f"✅ Result: {text[:100]}...")
                elif isinstance(output, list) and len(output) > 0:
                    # Обработка формата list
                    first_item = output[0]
                    if isinstance(first_item, dict) and first_item.get('type') == 'text':
                        text = first_item['text']
                        if 'Error' in text:
                            print(f"❌ Error: {text}")
                        else:
                            print(f"✅ Result: {text[:100]}...")
                else:
                    # Запасной вариант — вывод необработанного результата
                    print(f"✅ Result: {str(output)[:100]}...")

            except (json.JSONDecodeError, AttributeError, KeyError) as e:
                # Запасной вариант — необработанный вывод, если разбор завершился ошибкой
                print(f"✅ Result: {str(chunk.item.output)[:100]}...")

        elif chunk.name == 'message_output_created':
            try:
                content = chunk.item.raw_item.content
                if content and len(content) > 0:
                    print(f"💬 Response: {content[0].text}")
            except (AttributeError, IndexError):
                print(f"💬 Response: {str(chunk.item)[:100]}...")

    # Текстовые дельты для стриминга
    elif (hasattr(chunk, 'type') and
          chunk.type == 'raw_response_event' and
          hasattr(chunk, 'data') and
          hasattr(chunk.data, 'type') and
          chunk.data.type == 'response.output_text.delta'):
        print(chunk.data.delta, end='', flush=True)

async with MCPServerStdio(
        name="ClickHouse SQL Playground",
        params={
            "command": "uv",
            "args": [
                'run',
                '--with', 'mcp-clickhouse',
                '--python', '3.13',
                'mcp-clickhouse'
            ],
            "env": env
        }, client_session_timeout_seconds = 60
) as server:
    agent = Agent(
        name="Assistant",
        instructions="Use the tools to query ClickHouse and answer questions based on those files.",
        mcp_servers=[server],
    )

    message = "What's the biggest GitHub project so far in 2025?"
    print(f"\n\nRunning: {message}")
    with trace("Biggest project workflow"):
        result = Runner.run_streamed(starting_agent=agent, input=message, max_turns=20)
        async for chunk in result.stream_events():
            simple_render_chunk(chunk)