Python on MCP Toolbox for Databases

ADK

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The toolbox-adk package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.

Installation

pip install google-adk[toolbox]

Usage

The primary entry point is the ToolboxToolset, which loads tools from a remote Toolbox server and adapts them for use with ADK agents.

Note

Core

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The toolbox-core package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.

Installation

pip install toolbox-core

Note

LangChain/LangGraph

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The toolbox-langchain package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.

Installation

pip install toolbox-langchain

Quickstart

Here’s a minimal example to get you started using LangGraph:

from toolbox_langchain import ToolboxClient
from langchain_google_vertexai import ChatVertexAI
from langgraph.prebuilt import create_react_agent

async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
 tools = toolbox.load_toolset()

 model = ChatVertexAI(model="gemini-3-flash-preview")
 agent = create_react_agent(model, tools)

 prompt = "How's the weather today?"

 for s in agent.stream({"messages": [("user", prompt)]}, stream_mode="values"):
 message = s["messages"][-1]
 if isinstance(message, tuple):
 print(message)
 else:
 message.pretty_print()

Tip

LlamaIndex

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The toolbox-llamaindex package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.

Installation

pip install toolbox-llamaindex

Quickstart

Here’s a minimal example to get you started using LlamaIndex:

import asyncio

from llama_index.llms.google_genai import GoogleGenAI
from llama_index.core.agent.workflow import AgentWorkflow

from toolbox_llamaindex import ToolboxClient

async def run_agent():
 async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
 tools = toolbox.load_toolset()

 vertex_model = GoogleGenAI(
 model="gemini-3-flash-preview",
 vertexai_config={"project": "project-id", "location": "us-central1"},
 )
 agent = AgentWorkflow.from_tools_or_functions(
 tools,
 llm=vertex_model,
 system_prompt="You are a helpful assistant.",
 )
 response = await agent.run(user_msg="Get some response from the agent.")
 print(response)

asyncio.run(run_agent())

Tip