Ferramenta de agente interpretador de código Python

Azure Databricks fornece system.ai.python_exec, uma função integrada do Unity Catalog que permite aos agentes de IA executar dinamicamente Python código escrito pelo agente, fornecido por um utilizador ou recuperado de uma base de código. Está disponível por padrão e pode ser usado diretamente numa consulta SQL.

SELECT python_exec("""
import random
numbers = [random.random() for _ in range(10)]
print(numbers)
""")

Para saber mais sobre as ferramentas do agente, consulte ferramentas de agentes de IA.

Adicione o interpretador de código ao seu agente

Para adicionar python_exec ao seu agente, ligue-se ao servidor MCP gerido para o system.ai esquema do Unity Catalog. O interpretador de código está disponível como uma ferramenta MCP pré-configurada em https://<workspace-hostname>/api/2.0/mcp/functions/system/ai/python_exec.

SDK de Agentes OpenAI (Aplicações)

from agents import Agent, Runner
from databricks.sdk import WorkspaceClient
from databricks_openai.agents import McpServer

# WorkspaceClient picks up credentials from the environment (Databricks Apps, notebook, CLI)
workspace_client = WorkspaceClient()
host = workspace_client.config.host

# The context manager manages the MCP connection lifecycle and ensures cleanup on exit.
# from_uc_function constructs the endpoint URL from UC identifiers and wires in auth
# from workspace_client, avoiding hardcoded URLs and manual token handling.
async with McpServer.from_uc_function(
    catalog="system",
    schema="ai",
    function_name="python_exec",
    workspace_client=workspace_client,
    name="code-interpreter",
) as code_interpreter:
    agent = Agent(
        name="Coding agent",
        instructions="You are a helpful coding assistant. Use the python_exec tool to run code.",
        model="databricks-claude-sonnet-4-5",
        mcp_servers=[code_interpreter],
    )
    result = await Runner.run(agent, "Calculate the first 10 Fibonacci numbers")
    print(result.final_output)

Conceda à aplicação acesso à função em databricks.yml:

resources:
  apps:
    my_agent_app:
      resources:
        - name: 'python_exec'
          uc_securable:
            securable_full_name: 'system.ai.python_exec'
            securable_type: 'FUNCTION'
            permission: 'EXECUTE'

LangGraph (Aplicações)

from databricks.sdk import WorkspaceClient
from databricks_langchain import ChatDatabricks, DatabricksMCPServer, DatabricksMultiServerMCPClient
from langgraph.prebuilt import create_react_agent

workspace_client = WorkspaceClient()
host = workspace_client.config.host

# DatabricksMultiServerMCPClient provides a unified get_tools() interface across
# multiple MCP servers, making it easy to add more servers later without refactoring.
mcp_client = DatabricksMultiServerMCPClient([
    DatabricksMCPServer(
        name="code-interpreter",
        url=f"{host}/api/2.0/mcp/functions/system/ai/python_exec",
        workspace_client=workspace_client,
    ),
])

async with mcp_client:
    tools = await mcp_client.get_tools()
    agent = create_react_agent(
        ChatDatabricks(endpoint="databricks-claude-sonnet-4-5"),
        tools=tools,
    )
    result = await agent.ainvoke(
        {"messages": [{"role": "user", "content": "Calculate the first 10 Fibonacci numbers"}]}
    )
    # LangGraph returns the full conversation history; the last message is the agent's final response
    print(result["messages"][-1].content)

Conceda à aplicação acesso à função em databricks.yml:

resources:
  apps:
    my_agent_app:
      resources:
        - name: 'python_exec'
          uc_securable:
            securable_full_name: 'system.ai.python_exec'
            securable_type: 'FUNCTION'
            permission: 'EXECUTE'

Serviço de Modelos

from databricks.sdk import WorkspaceClient
from databricks_mcp import DatabricksMCPClient
import mlflow

workspace_client = WorkspaceClient()
host = workspace_client.config.host

mcp_client = DatabricksMCPClient(
    server_url=f"{host}/api/2.0/mcp/functions/system/ai/python_exec",
    workspace_client=workspace_client,
)

tools = mcp_client.list_tools()

# get_databricks_resources() extracts the UC permissions the agent needs at runtime.
# Passing these to log_model lets Model Serving grant access automatically at deployment,
# without requiring manual permission configuration.
mlflow.pyfunc.log_model(
    "agent",
    python_model=my_agent,
    resources=mcp_client.get_databricks_resources(),
)

Para implementar o agente, veja Implementar um agente para aplicações de IA generativa (Model Serving). Para detalhes sobre agentes de registo com recursos MCP, veja Utilizar servidores MCP geridos pela Databricks.

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