Como criar um agente de IA com LangChain/LangGraph usando o servidor MCP do ClickHouse.

pip install -q --upgrade pip
pip install -q langchain-mcp-adapters langgraph "langchain[anthropic]"

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

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

server_params = StdioServerParameters(
    command="uv",
    args=[
        "run",
        "--with", "mcp-clickhouse",
        "--python", "3.13",
        "mcp-clickhouse"
    ],
    env={
        "CLICKHOUSE_HOST": "sql-clickhouse.clickhouse.com",
        "CLICKHOUSE_PORT": "8443",
        "CLICKHOUSE_USER": "demo",
        "CLICKHOUSE_PASSWORD": "",
        "CLICKHOUSE_SECURE": "true"
    }
)

class UltraCleanStreamHandler:
    def __init__(self):
        self.buffer = ""
        self.in_text_generation = False
        self.last_was_tool = False
        
    def handle_chunk(self, chunk):
        event = chunk.get("event", "")
        
        if event == "on_chat_model_stream":
            data = chunk.get("data", {})
            chunk_data = data.get("chunk", {})
            
            # Processar apenas conteúdo de texto real, ignorar streams de invocação de ferramentas
            if hasattr(chunk_data, 'content'):
                content = chunk_data.content
                if isinstance(content, str) and not content.startswith('{"'):
                    # Adicionar espaço após a conclusão da ferramenta, se necessário
                    if self.last_was_tool:
                        print(" ", end="", flush=True)
                        self.last_was_tool = False
                    print(content, end="", flush=True)
                    self.in_text_generation = True
                elif isinstance(content, list):
                    for item in content:
                        if (isinstance(item, dict) and 
                            item.get('type') == 'text' and 
                            'partial_json' not in str(item)):
                            text = item.get('text', '')
                            if text and not text.startswith('{"'):
                                # Adicionar espaço após a conclusão da ferramenta, se necessário
                                if self.last_was_tool:
                                    print(" ", end="", flush=True)
                                    self.last_was_tool = False
                                print(text, end="", flush=True)
                                self.in_text_generation = True
                                
        elif event == "on_tool_start":
            if self.in_text_generation:
                print(f"\n🔧 {chunk.get('name', 'tool')}", end="", flush=True)
                self.in_text_generation = False
                
        elif event == "on_tool_end":
            print(" ✅", end="", flush=True)
            self.last_was_tool = True

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await load_mcp_tools(session)
        agent = create_react_agent("anthropic:claude-sonnet-4-0", tools)
        
        handler = UltraCleanStreamHandler()        
        async for chunk in agent.astream_events(
            {"messages": [{"role": "user", "content": "Who's committed the most code to ClickHouse?"}]}, 
            version="v1"
        ):
            handler.handle_chunk(chunk)
            
        print("\n")