How to Use the Amazon S3 MCP in Google ADK

Q: Can I limit the Amazon S3 tools available to my Google ADK agent?

You can use the toolnames filter in your McpToolset configuration. This restricts your agent to only the specific bucket actions you want to expose.

Q: Is Amazon S3 compatible with Google ADK long-context reasoning?

Absolutely. You can fetch file content via getobjectdata and feed it into a Gemini agent with 1M+ tokens. It connects your data directly to the model's memory.

Integrate Amazon S3 storage into your Google ADK agent workflows for enterprise-grade data orchestration.

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Connect Amazon S3 MCP to Google ADK

Create your Vinkius account to connect Amazon S3 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 Amazon S3 with Google ADK

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Google ADK storage management

You can now bridge your Google Cloud infra with your Amazon S3 buckets. Tools like `create_bucket` allow your agent to prepare storage environments on the fly. It connects natively to the Google ADK, so you can mix storage commands with your existing BigQuery data processing. Your agent handles the heavy lifting across clouds.

Automated object handling in Google ADK

Use `put_object` and `delete_object` to keep your remote files in sync with your local agent state. It is perfect for offloading high-volume data from your Gemini-powered pipelines. Your agent can trigger these tools based on complex reasoning. The server responds quickly, allowing your long-context agents to maintain momentum.

Metadata visibility for Google ADK

Retrieve object details with `get_object_metadata` to inform your agent's logic. You can use these insights to decide which files to process next within your Google Cloud ecosystem. List your assets with `list_objects` to map your remote directory. It makes your agent's awareness of your storage footprint immediate and precise.

Setup guide

Set up Amazon S3 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 Amazon S3 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="Amazon S3_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Amazon S3 tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Amazon S3 MCP in Google ADK

Configure your server parameters using the Vinkius endpoint. The McpToolset will then make your buckets available as native tools in your agent code.

You can use the tool_names filter in your McpToolset configuration. This restricts your agent to only the specific bucket actions you want to expose.

Absolutely. You can fetch file content via `get_object_data` and feed it into a Gemini agent with 1M+ tokens. It connects your data directly to the model's memory.

Vinkius handles the underlying infrastructure for you. You only need the server URL and your endpoint token to start calling functions from your framework.

Your file information is passed through a secure, ephemeral tunnel. We ensure that only authorized agent instances can query your object attributes.

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