How to Use the Box MCP in Google ADK

Q: Is it possible to search Box from a Google ADK agent?

Use the searchcontent tool to query your files by name or type. The agent feeds these results into its context window, making your Box documents instantly searchable.

Q: Can Box folders be managed through Google ADK?

Absolutely. You can create new directories using createfolder and move items as needed. The MCP Server translates your agent's intent into the correct API calls.

Connect Box to your Google ADK pipelines for high-context document reasoning and automated cloud storage management.

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Connect Box MCP to Google ADK

Create your Vinkius account to connect Box to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Box with Google ADK

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Native Box integration for Google ADK

Link your cloud storage directly into your Gemini-powered agents. The `list_folder_items` tool lets your agent index your files to prepare data for large-context reasoning. Since this runs on Google ADK, you can chain these actions with your existing BigQuery workflows. It turns your file store into an active part of your agent's data pipeline.

Update Box attributes via Google ADK

Rename files or modify their descriptions on the fly using `update_file`. This is helpful when your agent processes documents and needs to tag them based on analysis. Your agent maintains the state of your cloud storage without you writing manual scripts. It updates the metadata in real-time, keeping your file organization consistent.

Secure file lifecycle with Google ADK

Clean up your environment by executing `delete_file` directly from your agent logic. You can set the recursive flag on `delete_folder` to wipe out entire project trees when a task concludes. This keeps your storage costs down and your workspace tidy. Your agent handles the cleanup, so you don't have to monitor the file system manually.

Setup guide

Set up Box MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Box tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Box_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Box tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Box MCP in Google ADK

Use the `search_content` tool to query your files by name or type. The agent feeds these results into its context window, making your Box documents instantly searchable.

Vinkius handles the auth handshake, so you only need to provide the endpoint token. Your Google ADK agent connects to the server and gains access to the tools without you managing keys.

Absolutely. You can create new directories using `create_folder` and move items as needed. The MCP Server translates your agent's intent into the correct API calls.

While the server interacts with your files, you can easily pass the retrieved metadata into your BigQuery processing agents. It bridges the gap between raw file storage and your data warehouse.

Your files are processed in an ephemeral V8 sandbox. The server reads the file metadata required for your task and discards the session once the request finishes.

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